Graduation Project Document - Vol 2

DEGREE PROJECT Design for Better Decisions Sponsor : Fractal Analytics Pvt. Ltd., Mumbai + Self

Volume : 2 of 2 STUDENT : MANAN PAHWA PROGRAMME : Bachelors of Design (B. Des)

GUIDE : SWEETY TAUR

2020 INDUSTRIAL DESIGN FACULTY (FURNITURE DESIGN)

The Evaluation Jury recommends MANAN PAHWA for the

Degree of the National Institute of Design IN INDUSTRIAL DESIGN (FURNITURE DESIGN)

herewith, for the project titled "Design for Better Decisions" on fulfilling the further requirements by*

Chairman Members :

Jury Grade : *Subsequent remarks regarding fulfilling the requirements : This Project has been completed in ________________ weeks.

Activity Chairperson, Education

“There are enormously beneficial things that AI can do for us, especially when it gets linked with biology” - Yuval Noah Harari, Historian Author of Sapiens

Privacy Statement Copyright © 2020-2021 Student Document Publication meant for private circulation only.

All rights reserved.

No part of this document will be reproduced or transmitted in any form or by any mean, electronically or mechanically, including photocopying, xerography, photography and videography recording without written permission from the publisher, Manan Pahwa and National Institute of Design.

This project is partly sponsored by Fractal Analytics, Mumbai and partly self-sponsored Edited and Designed by Manan Pahwa Bachelor of Design, Furniture Design, 2016-2021 National Institute of Design, Ahmedabad mananpahwaa@gmail.com Guided by Sweety Taur Processed at the National Institute of Design (NID) Paldi, Ahmedabad - 380007 Gujarat, India www.nid.edu

© NID 2020

Originality Statement I hereby declare that this submission is my own work and it contains no full or substantial copy of previously published material, or it does not contain substantial proportions of material which have been accepted for the award of any other degree or final graduation of any other educational institution, except where due acknowledgement is made in this graduation project. I also declare that none of the concepts are borrowed or copied without due acknowledgement. I further declare that the intellectual content of this graduation project of my own work, except to the extent that assistance fromothers in the project’s design and conception or in style, presentation and linguistic expression is acknowledgement. This graduation project (or any part of it) was not and will not be submitted as assessed work in any other academic course.

Student Name in Full Signature Date

I hereby grant the National Institute of Design the right to archive and to make available my graduation project / thesis / dissertation in whole or in part in the Institute’s Knowledge Management Centre in all forms of media, nor or hereafter known, subject to the provisions of the Copyright Act. I have either used no substantial portions of copyright material in my document or I have obtained permission to use copyrighted material.

Signature Date

Design for Better Decisions Using AI, Engineering & Design to deliver a fatigue-free movie-choosing experience

Volume 2 | Designing the Thing Right

VOLUME 1

VOLUME 2

Opportunities, Strategy, Targeted Research, Synthesis

Design directions, prototyping, outcome

DESIGNING THE RIGHT THING

DESIGNING THE THING RIGHT

Section 1 Section 2 Section 3 Section 4 Section 5 Section 6

• • • • • • • • • •

Introduction Opportunity

Section 7

Strategy

Section 8

Discover

8b

COVID hit Define

• Introduction • Ideate • Prototype, Hypotheses & Evidences

• Epilogue • Appendix

End of Internship Empathise Synthesis Recap

VOLUME 1 is a process map of achieving the final use-case to build solutions on in VOLUME 2. It focuses on the navigation from multiple diverging and converging diamonds - Opportunities to Strategy, Discover to Define, Empathise to Synthesize

VOLUME 2 is the action map to answer the How Might We statement achieved at the end of VOLUME 1 with a final design proposal. It is a WIP journey of manifesting ideas, user-journey, phygital mockup and answers the hypotheses that emerged from the user-journey.

Process OPPORTUNITY

FIG 7.00 DESIGN PROCESS

Experience Design precedent study

DISCOVER

DEFINE

Deconstruct the process

Ethnography

Co-Ideation workshop

Prioritising

Decision Matrix

Insights

empathising with user decisions

Client Feedback

AED

Client feedback

Research Directions

Ideation 1.0

Rip the brief

Blue Sky thinking

Ideation 2.0

Principles

8-phase design process followed from Client brief to arriving at a solution. Detailed process with sub-phases has been shared. Each process has been accompanied by the page number in the respective document for the reader to deep-dive. It is recommended to use this as a guide to understand the macroview of the project. This diagram is based on double-diamond model.

STRATEGY

interesting spin

Client Advisory Board (CAB) User-journey mapping

identify directions

Opportunity areas

Ideas 2.0

Clustered topics

Strategic Brief

Initial Ideas with feedback

DESIGNING THE RIGHT THING

Solution D

Direction

EMPATHISE

DESIGN

SYNTHESIZE

DELIVER

212

Directions/ 26 Sketching Competitor Analysis

Iterate

Market Gap

14

Touchpoints 24

208

Conclusions

39

Storyboarding

Evidences

Discussion Guide

HMW

80

User- and expert-feedback

User research

18

Use-case

Desk Research & Prototype

Insights

Shadowing

User-journey mapping

Interviews

Pesrona building

Affinity Mapping

Gaps & Opportunities

66

Market research

49

Secondary Research

Concept

Use-cases & validation

Unstructured Findings

Hypotheses

How might we 14 Statement

Concept with Hypotheses 50

DESIGNING THE THING RIGHT

User - journey, Artefact, 210 Validations for hypotheses

Contents Privacy Statement Originality and Copyright Statement Introduction to Volume 2 of 2 Process CONTENTS

6 7 8 10 12

7/Design

16

8a/Deliver

50

7.1 Shadowing 7.2 User-flow 7.3 Touchpoints 7.4 Directions 7.5 User & Guide Feedback 7.6 Storyboarding Pillouse Mirror Friend 7.7 Concept Selection

18 20 24 26 38 39 40 46 49

8.1 Solution Blueprint 8.2 Physical Interface 8.3 User-flow 8.4 UX Blueprint 8.5 Hypotheses in UX Blueprint 8.6 Summary of Hypotheses 8.7 Evidences 8.8 Summary of Conclusion 8.9 Closure

52 56 66 72 74 76 80 208 210

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8b/Evidences

80

Epilogue

212

(1) Full Scale Prototyping (2) Automatic detection of hand (3) Preference of content based on the current affective state (4) Phyiological indicators and Data Collection, Emotion Recognition using physiological data, Experiment with sensors (5) Somnox Sleep Robot, Synchronisation of user’s breathing with the pillow (6) Breathing and Better Decisions (7) Managing attention via interface (8) Capturing nuanced human preferences (9) Colors and affect (10) Deploying at Scale, Training with Individual feedback (11) Finding content with interface design, Gut-based decision making (12) Data-driven approach to match movies and colours (13) Sensing Touch

82 116 118

Reflections Glossary of terms References Contributors Colophon Contact Information

214 216 218 226 228 229

DESIGN FOR BETTER DECISIONS

120

138 140 142 156 164 168 174 200 206

DESIGNING THE THING RIGHT

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Volume 1 (of 2) culminated the with a Use-Case and How Might We (HMW) statement. This HMW statement is the tipping point that will be addressed through an action map in this volume.

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How might we help the user in a mentally exhausted state to be able to choose something to watch while alleviating indecision?

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DEFINE EMPATHISE SYNTHESIZE

7.

DESIGN DELIVER EPILOGUE

This section addressed the process followed while ideating solutions for address the HMW statement. It showcases the navigation process of sketching multiple ideas, and finally arriving at iteration of an idea to go ahead with.

7.1 Shadowing 7.2 User-flow 7.3 Touchpoints 7.4 Ideas 7.5 User & Team feedback 7.6 Storyboarding Pillouse, Mirror Friend 7.7 Concept Selection

Shadowing as a generative research method

Shadowing was used as a research method to (a) Understand the user-journey from end of workday to the moment of movie-choosing all the way to the end of day and

This process helped in empathising better with the users and list down potential touchpoints which are actually activities performed by the user in a repetitive mundane manner. These potential touchpoints will be handy to initiate / intervene and support the ideation stage.

(b) Generate concepts for intervention.

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FIG 7.01 SHADOWING THE USER FOR A DAY

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SCENARIO I Work from Home While wor k i n g

E n d of wor k d a y

Gets food

V a l i d a ti on fr om oth er

Somya is a 22 year old young professional living in Karnal, India. She works as a business analyst at McKinsey and Co. Somya works on different projects lasting about 12-16 weeks with different teams. She usually operates from 10 a.m. - 8 p.m.

Before exploring Exploring what to watch Post exploring

G o es fo r a wa lk w/o w a t c h i n g

Fo o d g ets co ld

FIG 7.02 SOMYA’S END OF DAY JOURNEY

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S w i t c h on TV

G o ogle Se ar c h

DESIGN FOR BETTER DECISIONS

T alk to a f riend

Interacts w i th Fi r esti ck

Hom escr een

Trig g ers an d p r efer en ces

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SCENARIO II Running a business Enter hom e

Rem ov es m a sk

Dro p it m i d w a y

S cr ol l

Siddharth is a 22 year old businessman working in food and clothing sector. He works a lot and at the day-end likes to watch something while having dinner. His work-day starts at 10 am and ends at around 11. He is a social extrovert and fond of travelling for a long-weekend vacation with his friends every 6-8 weeks.

Before exploring

Post exploring

Exploring what to watch

S l eep

Re-w a tch o ld sho ws

FIG 7.03 SIDDHARTH’S END OF DAY JOURNEY

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G e t s fr e s h

Gra b d inner

Ente r Room

S etup La p top

O T T Plat for m

H o ld the cushio n

Sit o n th e b ed

Ch a n ge cl oth es

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Touchpoints Various touchpoints were used as tipping points for ideation

The interfaces (like cushion, mat, mirror) identified within the user-journey were re-imagined as future phygital interfaces with the power of Artificial- and Human-Intelligence (AI + HI).

A total of VI ideas were explored.

SCENARIO I Work from Home Whil e working

End of work day

Switch on TV

Ge t s food

Validation from other

Google Search

Talk to a friend

Interacts with Firestick

Homescreen

Somya is a 22 year old young professional living in Karnal, India. She works as a business analyst at McKinsey and Co. Somya works on different projects lasting about 12-16 weeks with different teams. She usually operates from 10 a.m. - 8 p.m.

Before exploring Exploring what to watch Post exploring

Goes for a w a l k w/o w a t c h i n g

Food ge t s col d

Triggers and preferences

FIG 7.04 TOUCHPOINTS IN SOMYA’S JOURNEY

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24

SCENARIO II Running a business Ent e r ho m e

Re m o ve s m a s k

Ge t s f r e s h

Gr ab d inne r

Ent e r Ro o m

Se t up Lap t o p

Drop it midway

S c r o ll

O TT P la t fo r m

H o l d t he c ushio n

Sit o n t he b e d

C hang e c l o t he s

Siddharth is a 22 year old businessman working in food and clothing sector. He works a lot and at the day-end likes to watch something while having dinner. His work-day starts at 10 am and ends at around 11. He is a social extrovert and fond of travelling for a long-weekend vacation with his friends every 6-8 weeks.

Before exploring

Post exploring

Exploring what to watch

S l eep

Re-watch old s h ows

FIG 7.05 TOUCHPOINTS IN SIDDHARTH’S JOURNEY MANAN PAHWA • NATIONAL INSTITUTE OF DESIGN

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DESIGN FOR BETTER DECISIONS

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25

DIRE C T I O N I

WATCH ME Watch Me is a smart interface that uses you hand as an Input & Sensing Platform and reduce your burden of choosing what video to watch next!

Watch Me helps the user to internalise their current mood and provide prompts for dealing with the mood.

Gives a common language to be talked about

FIG 7.06 USER REMOVING MASK AFTER REACHING HOME POST WORK

FIG 7.07 WATCH-ME’ IDEA SKETCH

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DIRE C T I O N I I

SQUEEZE ME The Squeeze-meter is a qwerky & fun interface to convert the seemingly effortful activity of understanding how you’re feeling and thinking of a movie to watch and turning it into a fun & personalised one.

It helps the user to internalise their current mood and provide prompts for dealing with the mood.

After choosing, a set of 3 options helps the user choose quickly with keywords suggesting them the vibe of the movie.

FIG 7.08 USER HOLDING THE CUSHION

FIG 7.09 SQUEEZE-ME’ IDEA SKETCH

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DIRE C T I O N I I I

FAVOURITE! If you are an explorer, there are numerous objects and findings that excite you everyday and make you want know more about them. What if you could save them for later and watch related videos to them?

Favourite! is a handy plugin that you can use to scan interesting objects and text. Get ready to find movies related to different attributes of these interesting objects.

FIG 7.10 TABLE COVERED WITH OBJECTS

FIG 7.11 FAVOURITE!’ IDEA SKETCH

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DIRE C T I O N I V

MIRROR FRIEND Usually when you feel cognitively depleted after work and tired, one of the ways of taking effective breaks is by talking to a friend. Well, what if your mirror is that friend?

Talk about the day to your mirror friend and they’ll recommend you a movie to watch.

FIG 7.12 USER WASHING FACE IN THE WASHROOM AFTER A TIRING DAY

FIG 7.13 MIRROR FRIEND’ IDEA SKETCH

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DIRE C T I O N V

HAND-PICKED Humans convey non-verbally through their hands a lot often than we notice. Hand-picked is a handsensing tech that understands your daily moods and helps the recommendation engine give better suggestions.

A lot of our conversations have non verbal cues that enhance the conversation The goal is to get a contextual understanding of the user with a non-conscious design intervention.

FIG 7.14 HAND PICKED’ IDEA SKETCH

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DIRE C T I O N VI

CHAMBER OF EMOTIONS Based on the famous diary from Harry Potter’s Chamber of Secrets - Chamber of Emotions gives back a movie recommendation when you write about your day in it.

Diary writing is a healthy habit. What if that healthy habit could be more rewarding than being able to give a rest to your thoughts? Get a movie recommendation with it.

Writing with hand is one of the best communication methods to get a nuanced understanding about a user’s personalities and how they are feeling.

FIG 7.15 NOTEBOOK KEPT ON THE TABLE

FIG 7.16 CHAMBER OF EMOTIONS’ IDEA SKETCH

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DESIGN FOR BETTER DECISIONS

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User feedback

Guide Feedback

These intervention ideas were presented to the users.

Right gap is to explore phygital experiences. The exploration of touch as a sense in digitally-intense experiences is going to be unique and holds great value. The gap is quite strong in that competitor solution space.

Watch-me, Squeeze me, Mirror Friend and Hand-picked were the ones that sounded exciting to them. Each idea had its pros and cons.

SQUEEZE ME could be developed further by reimagining ASMR surfaces as decision-making interfaces.

WATCH-ME

MIRROR FRIEND

SQUEEZE ME

HAND-PICKED

+

+

+

+

It provides easy integration with available

Innovation with a familiar interface will be

Destressing and fun interaction - Users

Non-conscious, least effort; More pleasure,

devices and help ascertain a positive/negative

taken well by consumers.

don’t have to think and explicitly input their

less pain

emotion

Can be imagined as an integrated spacial

choices, which helps in replenishing cognitive

Has potential to be integrated with other ideas.

experience

resources

-

More interactions could be tested - rotate,

Hand gesttures / bdy langugae alone are

-

-

hold, pressure, temp, acceleration with ball,

incomplete parameters to derive emotions.

There is still dependability on the user in terms

In a Post-covid scenario, building a human-like

fidget spinner

In some contexts, and-gestures can be

of explicitly validating how they feel.

personality to an inanimate object might lead to unprecedented social consequences.

completely negated and other body language -

parameters overplay.

How will the ball be able to differentiate between states of Stress, Angry, Anxious, Restless & excited (all being in a similar elevated brain condition)

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Storyboarding DESIGN FOR BETTER DECISIONS

Based on user and Industry guide feedback, 2 ideas were taken ahead and storyboarded.

1, SQUEEZE ME reimagined and developed as PILLOUSE 2, MIRROR FRIEND

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CONCEPT I PILLOUSE

Tangible rejuvenating interfaces

Tangible = able to touch and hold Rejuvenating = instilling a sense of new, revitalise A lot of objects / tangible interfaces which instill calmness and are revitalising interactions were collated into a moodboard with a Most of these objects are made with materials which are rejuvenating either visually or in a tactile way.

Worry stone - indented gemstones with natural shine, Cutting sand, Clean parallel lines on sand, Scooping sand, Water droplets joining together, handheld water ring game, squeezy balls, soft toys, jelly sticks sliced with knife. This is where the idea of a mood-detection cushion sparked.

FIG 7.17 MOODBOARD CREATED FOR TANGIBLE REJUVENATING SURFACES

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Concept

PILLOW + MOUSE = PILLOUSE Remember the moment when you hugged someone you love? Do you remember hugging your pillow tight when you felt low?

Imagine an interface which understands how you’re feeling withou you explicitly telling it?

Pillouse (Smart-pillow) is a fun & calm interface which converts this seemingly effortful activity of choosing and turns it into a playful & tactile one.

FIG 7.18 WOMAN HUGGING A PILLOW WHILE SLEEPING

FIG 7.19 STORYBOARD FOR PILLOUSE CONCEPT (I)

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User enters home in a quite mentally exhausted state

They sit on the furniture in a relaxed way

They switch on the TV

Reach out to PILLOUSE

Hug it tighly while holding it. Cuddle with it.

Ease stress. Feel relaxed.

The way user clutches the pillow tight explains how they are feeling. Pillow acts as a physical interface to understand how user is feeling

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Start breathing in a focused way.

Follow breathing rhythms of the pillow. Breathe in while it inflates, breathe out when it deflates.

The user feels relaxed after breathing for a while.

In parallel, the interface starts recognising their physiological data.

It starts with a few no. of tiles

The tiles animate as they were entering a funnel and getting filtered.

3 movies come out as a recommendation gift-wrapped for the user

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The Science The activity of deep breathing is a proven relaxation technique.

Physiological data can be used to detect emotion.

FIG 7.21 DEEP BREATHING (ARTICLE)

FIG 7.23 HEART RHYTHM PATTERNS DURING DIFFERENT PSYCHOPHYSIOLOGICAL STATES.

FIG 7.22 RELAXATION TECHNIQUES (HARVARD HEALTH)

FIG 7.24 RECORDINGS OF PROTOTYPICAL BREATHING PATTERNS FOR EACH BASIC EMOTION.

FIG 7.20 STORYBOARD FOR PILLOUSE CONCEPT (II)

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CONCEPT II MIRROR FRIEND

Concept

Usually when you feel cognitively depleted and tired after work, one of the ways of taking effective breaks is by talking to a friend / room-mate.

Well, what if your mirror is that friend?

FIG 7.25 THE MRS. BAND - MAGIC MIRROR

FIG 7.26 MESSAGES WRITTEN ON GLASS BY WATER DROPLETS

FIG 7.27 STORYBOARD FOR MIRROR FRIEND CONCEPT

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After returning home, user gets freshened up in the bathroom

While washing their face, that is when most of the thoughts bubble up in the head.

The user feels relaxed after unwinding about their day.

DESIGN FOR BETTER DECISIONS

User sits for watching TV

Snap the finger to switch on the mirror friend interface

Switch it on

The mirror asks about the user’s day, user describes their day vividly to the mirror. The mirror understands the mood via tonality.

3 movies come out as a recommendation gift-wrapped for the user

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The Science

What happened during the day is an important contextual factor for how the user is feeling at the end of the day

Putting feelings into words (affect-labelling) produces therapeutic effects in the brain

Analysing Analysing emotional-states and communication styles with the help of cutting-edge tools

“I felt low after scoring grades in a test. I wanted to feel motivated, and understand that this is not the end of the world!”

- an interviewee while describing how she constructs her movie choices FIG 7.28 NYT ILLUSTRATION FOR PUTTING FEELINGS INTO WORDS

FIG 7.29 CARDBOARD BOX POWERED

BY IBM WATSON

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Concept choosing DESIGN FOR BETTER DECISIONS

Mirror friend has a dystopic attribute to it.

Washroom is one of the most private spaces that a person can enjoy and be themselves. How does one behave in the presence of AI that is yet to be researched and tested. While Pillouse presents a good opportunity to be developed further, especially since it has a reason to believe and back it up (with scientific evidence).

PILLOUSE was taken ahead, contextualised, developed and renamed into EOD Buddy.

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8.1 Solution Blueprint 8.2 Physical Interface 8.3 User-flow 8.4 UX Blueprint 8.5 Hypotheses in UX Blueprint 8.6 Summary of Hypotheses

(1) Full Scale Prototyping (2) Automatic detetction of hand (3) Preference of content (4) Physiological Data & Emotions (5) Somnox Sleep Robot Sync of user’s breathing (6) Breathing and Better Decisions (7) Managing attention via interface (8) Capturing nuanced human preferences (9) Colors and affect (10) Deploying at Scale Training (11) Finding content with interface design (12) Datat-driven approach (13) Sensing Touch

8.7 Eviden

ces

EMPATHISE SYNTHESIZE DESIGN

8.

DELIVER EPILOGUE

This section is a journey of developing and delivering the solution. It contains Outcomes and Process of developing the solution.

The latter half of the section is a collection of evidences produced to conclude the hypotheses formed within the user-journey.

What’s the problem?

Fatigue & Movie Choosing

SS NE E IV

NEED FO R ATION VEN JU RE

Current experience of streaming content platform doesn’t help either. The gallery of tiles is too daunting and effortful to hunt and sort through

EAMING IN STR DE CI S

Tired at the end of day, users are unable to find content to watch. The current experience of streaming content platform doesn’t help either. The gallery of tiles is too daunting and effortful to hunt and sort through.

After a tiring day, the user is looking to rejuvenate and feel relaxed by watching something. However, that need remains unmet due to user dropping the idea of altogether.

C

Exersion during the day leads to depletion of cognitive resources, which is amplified by mindlessly scrolling on phone out of habit and dopamine kick.

TIVE FATIGU NI E OG

Vicious circle of inability to find streaming content to watch

FIG.8.01 VICIOUS CIRCLE

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Identified Gaps & Opportunities

1.

Touch deprivation

Touch deprivation (especially during the pandemic) is impacting humankind on a physiological and even a physical level.1 There’s an opportunity to fulfill this lack of touch with a multi-sensory experience, and helping users derail themselves by

4.

Analysis Paralysis

Humans overthink a lot even when it comes to taking a simple decision like ‘what to watch next?’. The perceived difficulty of the decision task is high & puts a heavy cognitive load on the user. This is called Analysis Paralysis, in simple terms Overthinking and it leads to diminished satisfaction.

The opportunity here is to design an interface that induces decision-making by user’s gut feelings and minimise the effort.

DESIGN FOR BETTER DECISIONS

2.

User’s affective-state is not accounted for Current algorithms would recommend content based on viewing history, however the identity of the user is fluid. They feel differently even during different times of the same day.

How a user is feeling is important to understand to recommend them a movie they’d like to watch.

5.

Choice Overload Rationally speaking, more choices = more customer needs being satisfied. However, that’s not true. Almost all OTT platforms are filled with endless no. of movie tiles which seems daunting to hunt through.

Overload of choice leads to diminished satisfaction as the user faces paradox of choosing. More choices = more time and effort, it also increases the uncertainity about their preferences.

3.

Explicit Verbal Inputs

Most humans are unable to articulate how they feel or what they would want to watch.

Moreover, there’s disassociation in how users actually feel, think and express.

There’s a interaction design opportunity to help users express via an interface without explicitly expressing everything

6.

Content Discovery remains a challenge

As the number of TV and video services increases, so does the average time spent searching for content – reaching almost one hour per day.

Current content discovery capabilities are failing to cope with consumers’ usage of multiple video services and devices, which is why 7 out of 10 consumers say a universal search feature would be very useful.2

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1.

Tactile Phygital experience

2.

Understanding preferences

by Physiological Data

Touch is the fundamental language of connection

Biosensors are used to capture raw data, processed by

(Dacher Keltner)3. At the center of the experience

emotion recogniton algorithm to understand the current

is the pillow which serves another purpose of a

affective state and emotional preferences from content.

physical interface to interact with the platform. It’s

This way, content choices are narrowed by eliminating the

ergonomic form, a unique Colour, Material & Finish

non-preferred content.

and snuggable form invites the user to relax by hugging it.

FIG.8.04 PHYSIOLOGICAL DATA ANALYSED IN REAL TIME

3.

Understanding needs from Implicit Inputs Along with the use of biosensors to narrow down

FIG.8.02 USER HUGGING THE PILLOW

preferences, during the next stage user’s preferences

EOD Bu FIG.8.05

are elicited by their response to the visual stimuli (colour). This is cross-validated with implicit data from the GSR sensor. There’s no dependency on the explicit verbal inputs, while non-verbal implicit inputs help in choosing movies.

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4.

Design for Gut-based Decision making

5.

Finite Scrolling

The interface has been designed in a playful manner that

The COLOUR PSYCHOLOGY RULE ENGINE prioritises

tricks the user’s mind into thinking they’re not taking a

colours displayed to the user by understanding the

decision, rather playing a game.

current and predicting the possible desired emotional states (personalised to each user). The Roulette UI &

Colours are used as a heuristic to choose movies. The user

scroll interaction includes dead-ends to non-consciously

can now take decisions from their gut. The user no longer has

nudge the user that options are limited.

to process a lot of information to finally convey what to watch.

FIG.8.07 LIMITED SCROLLING WITH COLOUR PALETTES

RIGHT NUMBER OF CHOICES Recommendations are delayed. No. of choices has been reduced from infinite to 3 at a moment, making it easier

uddy

for the user to choose. The experience is retractable.

FIG.8.06 ANIMATICS OF CHOOSING ANCHOR COLOUR UI

6.

Universal Search Feature

DESIGN FOR BETTER DECISIONS

50+ Content-streaming platforms like Netflix, Prime videos, Hotstar, Hulu.. have been integrated with the platform using APIs. They are now just an action buttons away from the user.

FIG.8.08 MOVIE RECOMMENDATION BASED ON COLOUR PALETTE

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EOD Buddy Your fatigue-free movie choosing companion

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FIG 8.09

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508 mm

712 mm

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Ergonomic Form Designed keeping your comfort at the core

310 mm

FIG 8.10

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Tactile Materials Pleasurable surfaces that help you rejuvenate yourself after a tiring day

FIG 8.11

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Designed till the last detail.

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Snug Fit

With an interactive

Click Wheel control MANAN PAHWA • NATIONAL INSTITUTE OF DESIGN

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FIG 8.12

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FIG 8.13 VARIOUS VIEWS OF USER-INTERACTION

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User flow Visual depiction of a 4-part user journey. With the phygital experience, the user is able to choose a movie to watch in a fatigue-free manner.

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Approach

The user settles down after the End of workday, approaches and starts interacting with the pillow in a playful manner.

Rejuvenate

User rejuvenates while hugging with the pillow. The visual experience declouds the mind and positions it in sub-conscious calming level and use implicit inputs to prioritise desirable options.

Play

In the play phase, the user explores by playing a game where they design a colour palette.

They perform all these actions via the pillow. This phase ends with a limited recommendations..

Watch

After a playful-interactive experience, the user is finally able to watch a movie.

At the end of the experience, they give feedback to the recommendation engine.

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Approach

The user settles down after the End of workday, approaches and starts interacting with the pillow in a playful manner.

1. You enter the room after a long tiring workday. You keep your bag, grab your dinner and are ready to watch a new movie

2. You sit on your bed and reach out to EOD Buddy.

3. It’s soothing form and finish invites the user to touch and explore it further. The user puts their hand in the pocket.

to end your day.

FIG 8.14 EOD BUDDY - USER FLOW

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Rejuvenate

User rejuvenates while hugging with the pillow. The visual experience declouds the mind and positions it in sub-conscious calming level and use implicit inputs to prioritise desirable options.

Simple meditative forms to position in subconscious calming zone

4. Within the pocket, you start playing with the playful and

5. The screen gradually lights up after you put your hand in the

6. While watching the screen, the pillow starts breathing slowly.

pleasurable texture. Intetracting with it makes you feel relaxed.

pocket with meditative forms; positions you in a subconscious

You can feel the expansion and contraction on your chest and

calming zone.

hands, you start breathing with it.

7. The visuals on the screen simulate and guide you to breathe

8. You gradually feel fresh, and declouded in your mind excited

9. A colour pops up on the screen after a while, this is the

at a calmer rate, helping you rejuvenate from the tiring day.

for the next stage.

colour representing how you’re feeling now.

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Play

In the play phase, the user explores by playing a game where they design a colour palette.

They perform all these actions via the pillow.

10. A stream of colours (mostly pastels) load on the screen one after the other.

11. The click wheel is located at the center

12. Similarly, you use your gut-feeling to do this for 4 times

You use the click wheel to explore the spectrum and pick an anchor colour you

of the pocket. To pick a colour, you tap the

arriving at a beautiful color palette. This composition is an

instinctively feel like in the moment.

click wheel.

abstraction of what your mind is feeling inclined towards.

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Watch

After a playful-interactive experience, the user is finally able to watch a movie.

At the end of the experience, they give feedback to the recommendation engine.

13. You get 2-3 movies recommended whose colour palette

14. You watch the trailer, you like it and you’ve finally got the

is similar to yours. The play buttons helps you quickly watch

movie without the hassle of endlessly scrolling through for

trailers to decide the one you’re going to watch tonight.

hours.

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USER-JOURNEY

UX Blueprint

The user Journey helps to see the big picture (end-to-end) of the whole solution is implemented by the service provider and used by the users with pinpointed dependencies.

USER ACTION

Conscious

FRONTEND FEEDBACK

Sits on the furniture (couch / bed) and gets ready to watch a new movie

Picks up the pillow / cushion

Explore the pillow

Physical Interface

Rejuvenate Puts hands in the pocket

Hug it tightly

subconscious calming starts

A pillow that invites the user

Tactile pockets entice the user to explore

sensors in the pocket

A comfortable, huggable pillow

Pillow starts breathing

USER ACTION

Digital Interface

Steps, choices, activities that the user performs while interacting with the experience to find a movie to watch. They are divided into 2 parts based on the consciousness of the user.

TV switches on

Responsive breathing visuals

Displays calming visuals

FRONTEND Interface that is directly in the view of the viewer. Since the experience is a phygital one, it has been colour coded into two types : Physical (pillow) & Digital (on screen) interface.

Enters the room

The user settles down after the End of workday, approaches and starts interacting with the pillow in a playful manner

Sub-conscious

USER PHASE A macroview / journey level view of the user-experience.

There are 4 user-phases in the end-to-end journey.

Approach

USER PHASE

BACKEND

GSR sensor detects activity

Detect breathing rate

Robotic mechanism fo simulating breathing

Record raw physiological data in real time using sensors

Raw data information extraction

BACKEND Everything that happens behind the scenes to supports to the frontend experience. The backend has been divided (and colour coded) into multiple clubbed entities for eg.

Emotion recognition algorithm

FIG 8.15 EOD BUDDY - USER JOURNEY

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User gets rejuvenated while hugging with the pillow. The visual experience declouds the mind and positions it in sub-conscious calming level and use implicit inputs to prioritise desirable options.

Breathe along the physical rhythm of the pillow

Feels relaxed and rejuvenates decisionmaking capability

Gets aware of the current emotional state

Starts breathing slowly sub-consciously

Play Pick an anchor colour most inclined towards

Watch

In the play phase, the user explores by playing a game where they design a colour palette. They perform all these actions via the pillow. This phase ends with a limited recommendations..

Choose the colour you find interesting and relatable with your gut feeling

Design a colour palette

Gets recommendations - trailer plays

Like it

Don't like it

Tap with the scroll wheel

Scroll back

Find a movie of your choice

After a playful-interactive experience, the user is finally able to watch a movie. At the end of the experience, they give feedback to the recommendation engine.

Watch a movie

Give feedback

Movie plays on the streaming platform

Digital interface for feedback

Sub-consciously chooses the mood

Breathing frequency of the pillow lowers

Pillow scroll wheel to scroll and clutch to select the colour

g

or

Screen starts conversing with the user keeping them engaged

Engaging visuals

Responsive breathing visuals slow down following the pillow's rhythm

A roulette with a stream of colours appear specifically personalised to user's mood

Interface which helps the user take a decision based on gut-feeling

Movie recommendations appear and trailer autoplays

EMOTIONAL COLOUR : A blob of colour expands and takes over the screen

Smart timer and sensor to guide the rhythm to the desired calming rhythm subconsciously

Capacitance sensing

Colour representing different moods

Mechanical support

Color psychology rule engine

Current affective state

Emotion recognition algorithm

DESIGN FOR BETTER DECISIONS

Behaviour driven algorithm

Pain-pleasure principle

Colour palette

Similar movie colour palettes

Computer vision

Color psychology rule engine

Identified desired affective state Deconstructed dominant emotion

Retractable experience

Nudge to avoid overthinking by the user

Prioritised colours according to desired emotional state

Leading metric

Clutter-free playful user-experience

Movie colour palettes metadata

API to connect with Movie streaming platforms

Backend integration train on feedback

Computer vision

train on feedback

Behaviour-driven algorithm

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USER-JOURNEY

Hypotheses in UX Blueprint A list of hypotheses/initial assumptions was created to guide the research, USER PHASE analysis and exploration and provide direction for developing the solution.

Each hypothesis (marked in black) were concluded by following certain methodologies : USER ACTION

1

The pillow feels inviting enough for the user to be motivated to touch it

2

The pillow feels comfortable and huggable during interaction

3

The presence of a hand can be detected in the pocket

4

User’s current affective state is a factor in determining what they want to watch

5

It is possible to record and extract physiological data in real time while sitting

6

User’s current emotional state can be elicited using physiological data

7

These data-points are reliable indicators of the current affective state of the user (a) individually (b) in combination

12

The set of algorithms will be able to identify user’s desired affective state & personalise recommendations

13

The feedback loop will help in personalising recommendations

14

FRONTEND FEEDBACK Colours represent how we feel and be Each individual’s difference in preferences and perception of colour can be accounted for by the algorithm

16

The machine will account for difference in colour calibrations in different screens

17

The way the interface is designed increases the chances of user finding content based on how they are feeling BACKEND

Enters the room

Sits on the furniture (couch / bed) and gets ready to watch a new movie

Picks up the pillow / cushion

Explore the pillow

Sub-conscious

Rejuvenate Puts hands in the pocket

Hug it tightly

subconscious calming starts

Physical Interface

A pillow that invites the user

1

Tactile pockets entice the user to explore

A comfortable, huggable pillow

sensors in the pocket

Pillow starts breathing

Digital Interface

8

19

An algorithm will be able to label movie-options according to their feel

9

Users will synchronize their breathing to the pillow’s rhythm

20

Click wheel can detect actions like tap, scroll and press

10

Breathing helps in rejuvenating cognitive resources and enhancing decision-making capability

21

11

Visual experience will (i) position the user in sub-conscious calming level and (ii) keep them engaged

OTT platforms can be connected to the recommendation platform for a seamless experience

Responsive breathing visuals

TV switches on

Displays calming visuals

GSR sensor detects activity

Users can take decision based on their gut-feeling thorugh an interface

The pillow can adjust its breathing according to the user

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Conscious

The user settles down after the End of workday, approaches and starts interacting with the pillow in a playful manner

used as heuristics for content-choices

15

18

Approach

3

Detect breathing rate

Robotic mechanism fo simulating breathing

Record raw physiological data in 4 real time using sensors

Raw data information extraction

FIG 8.16 HYPOTHESES IN USER-JOURNEY

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Play

User rejuvenates while hugging with the pillow. The visual experience declouds the mind and positions it in sub-conscious calming level and use implicit inputs to prioritise desirable options.

Feels relaxed and rejuvenates decisionmaking capability

Breathe along the physical rhythm of the pillow

10

Gets aware of the current emotional state

Starts breathing slowly 9 sub-consciously

2

Watch

In the play phase, the user explores by playing a game where they design a colour palette. They perform all these actions via the pillow. This phase ends with a limited recommendations..

Pick an anchor colour 17 most inclined towards

Choose the colour you find interesting and relatable with your gut feeling

Design a colour palette

Gets recommendations - trailer plays

Like it

Don't like it

Tap with the scroll wheel

Scroll back

Find a movie of your choice

After a playful-interactive experience, the user is finally able to watch a movie. At the end of the experience, they give feedback to the recommendation engine.

Watch a movie

Give feedback

Movie plays on the streaming platform

Digital interface for feedback

Sub-consciously chooses the mood

Breathing frequency of the pillow lowers

Pillow scroll wheel to scroll and clutch to select the colour

g

Responsive breathing visuals slow down following the pillow's rhythm

A roulette with a stream of colours 16 appear specifically personalised to user's mood

Smart timer and 8 sensor to guide the rhythm to the desired calming rhythm subconsciously

Colour representing different moods

Mechanical support

Identified desired affective state

Leading metric

Clutter-free playful user-experience

18

Movie recommendations appear and trailer autoplays

Retractable experience

Nudge to avoid overthinking by the user

Color psychology rule engine

5

Interface which helps the user take a decision based on gut-feeling

EMOTIONAL COLOUR : A blob of colour expands and takes over the screen

Prioritised colours according to desired emotional state

or

Screen starts conversing with the user keeping 11 them engaged

Engaging visuals

6

Current affective state

Deconstructed dominant emotion

Emotion recognition algorithm

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7

Behaviour driven algorithm

Pain-pleasure principle

Capacitance sensing

20

15

14

Colour palette

Computer vision

Color psychology rule engine

12

API to connect with Movie streaming 21 platforms

Similar movie colour palettes

Movie colour palettes metadata

19

Backend integration train on feedback

Computer vision

train on feedback

13

Behaviour-driven algorithm

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Summary of Hypotheses No.

Hypothesis

Approach 1

Methodology

Evidence

The user settles down after the End of workday, approaches and starts interacting with the pillow in a playful manner.

The pillow feels inviting enough for the user to be motivated to touch it

Rejuvenate

Conclusion

Secondary research - Benchmarking, Primary research - Prototyping-testing and user-feedback

The pillow’s unique combination of color, material and finish invites different users to touch and explore the pillow.

However, iterations are expected since it’s yet to be validated with a large group of users.

Section 1 Physical interface prototyping

User gets rejuvenated while hugging with the pillow. The visual experience declouds the mind and positions it in sub-conscious calming level and use implicit inputs to prioritise desirable options.

2

The pillow feels comfortable and huggable during interaction

Primary research - precedent study, ergonomic study, rapid prototyping & user-feedback

Hypothesis is true. The form of the pillow comfortable and huggable. However, iterations are expected since it is yet to be validated with a large group of users.

Section 1 Physical interface prototyping

3

The presence of a hand can be detected in the pocket

Primary research - prototyping & testing

A successful low-fidelity prototype of a functional capacitive sensor depicts that the hypothesis is true.`

Section 2 Automatic detection of hand

4

User’s current affective state is a factor in determining what they want to watch

Secondary research - Literature review, precedent study Primary research - interviews

Hypothesis is true.

Section 3 Preference of content based on the current affective state

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No.

Hypothesis

Methodology

Conclusion

Evidence

5

It is possible to record and extract physiological data in real time while sitting

Secondary research - Literature review, precedent study

Hypothesis stands true. Consumer electronic products have made it possible to record and extract physiological data in real time.

Section 4 Phyiological indicators and data collection

6

User’s current emotional state can be elicited using physiological data

Secondary research - Literature review, Primary research - Conceptualising (Due : Expert feedback)

There’s been reliable co-orelation between user’s current affective state and physiological signals combined. Theory of an emotion-recognition algorithm is in place, however needs to be validated further.

Section 4.1 Emotion Recognition using physiological data

7

These data-points are reliable indicators of the current affective state of the user (a) individually (b) in combination

Secondary research - Literature review, Primary research - prototyping & usability-testing

Hypothesis is partially true. The experiment concludes the potential however needs to be validated further.

8

The pillow can adjust its breathing according to the user

Secondary research - Literature review, benchmarking

Such technology exists that can detect user’s breathing and adjust its breathing according to the user and gradually slow down the breathing rhythm to a calming rate.

9

Users will synchronize their breathing to the pillow’s rhythm

Secondary research - Literature review

The hypothesis is true. A study shows a positive coorelation between user’s breathing rhythm and an object

10

Breathing helps in rejuvenating cognitive resources and enhancing decision-making capability

Secondary research - Literature review

The hypothesis stands true. Studies show the effects of breathing patterns on heart rate variability and decisionmaking in business cases.

Section 6 Breathing and Better Decisions

11

Visual experience will (i) position the user in sub-conscious calming level and (ii) keep them engaged

Primary research - Prototyping, (Due) usability testing

Fluid animation and calming uncomplicated visuals are used.

The design intervention needs to be tested in order to validate the hypothesis further.

Section 7 Managing attention via interface

DESIGN FOR BETTER DECISIONS

Section 4.2 Experiment with Sensors In order to validate the effectiveness of the combination of sensors mentioned in secondary research, a primary experiment was designed.

Section 5 Somnox Sleep Robot Details about the functioning of the product (Somnox) have been mentioned.

Section 5.1 Synchronisation of user’s breathing with the pillow

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Summary of Hypotheses No.

Hypothesis

Play 12

The set of algorithms will be able to identify user’s desired affective state & personalise recommendations

13

The feedback loop will help in personalising recommendations

14

Colours represent how we feel and be used as heuristics for content-choices

Methodology

Conclusion

Evidence

In the play phase, the user explores by playing a game where they design a colour palette.

They perform all these actions via the pillow. This phase ends with a limited recommendations..

Secondary research - Literature review, precedent study Primary research - Conceptualising

Theory is in place however the set of algorithms need to be tested in order to validate the hypothesis.

Section 8 Capturing nuanced human preferences Model of an algorithm is proposed. A case-study proves the possibility in real-life context.

Secondary research - Literature review

Each colour has the ability to evoke different emotions and various colors represent various moods.

Section 9 Colors and affect - The relationship is drawn along with applications in various fields.

Gap : The difference in preferences and perception are different for individuals and depend on a variety of factors. explored further in Hypothesis 16 15

Each individual’s difference in preferences and perception of colour can be accounted for by the algorithm

Secondary research - Literature review, expert review

Theory is in place however needs to be tested in order to validate the hypothesis.

More in Section 10 Deploying at scale

16

The machine will account for difference in colour calibrations in different screens

Primary research - Conceptualisation

Theory is in place however needs to be tested in order to validate the hypothesis.

More in Section 10.1.3 Training with Individual feedback

17

The way the interface is designed increases the chances of user finding content based on how they are feeling

Secondary research - Literature review, Primary research - prototyping

Theory is in place however usability-testing will validate the hypothesis. Interface is designed in a way that (1) Induces Gut-based decision-making (2) Reducing choice overload by limiting the no. of options

Section 11 Finding content with interface design - Development of the interface is shown

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No.

Hypothesis

Methodology

Conclusion

Evidence

18

Users can take decision based on their gut-feeling thorugh an interface

Secondary research - Literature review, Primary research - prototyping, usability testing through A-B testing

Theory is in place however usability-testing will validate the hypothesis. Studies suggest that users are able to take gutfeeling induced decisions when the System 1 of their brain is activated. Interface was designed in a way to induce feeling.

Section 11.2 Gut-based decision making Development of the interface is shown

19

An algorithm will be able to label movie-options according to their feel

Secondary research - Literature review Primary research - Conceptualisation

The algorithm model needs to be tested in order to validate the hypothesis.

Section 12 Data-driven approach to match movies and colours

20

Click wheel can detect actions like tap, scroll and press

Secondary research - Benchmarking, Primary research - Prototyping and testing

Hypothesis is true. The click wheel can detect actions like tap, scroll and press.

iPod is a reliable example to use Capacitive sensing capabilities. A successful low-fidelity prototype of a functional capacitive sensor was also developed in Section 13 Sensing Touch

Watch 21

OTT platforms can be connected to the recommendation platform for a seamless experience

DESIGN FOR BETTER DECISIONS

After a playful-interactive experience, the user is finally able to watch a movie.

At the end of the experience, they give feedback to the recommendation engine.

Secondary research - Benchmarking

Almost all of the content-streaming platforms like Netflix, Prime videos, Hotstar, Hulu.. have developed APIs (Application Programming Interface) which can be integrated with different platforms / apps / systems as action buttons for the user

Flickseeker is one of the competitors which use APIs to connect the recommended movie to redirect to the respective streaming platform in a single click (more in competitor analysis within Section 5)

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Evidences List of evidences produced to validate the hypotheses. Each evidence section starts with a hypotheses, methodology and ends with a conclusion. The list of sections are presented.

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Section

DESIGN FOR BETTER DECISIONS

Title

Hypotheses addressed

Page no.

S1 S2

Physical interface prototyping

1

Automatic detection of hand

3

116

S3

Preference of content based on the current affective state

4

118

S4

Phyiological indicators and Data Collection, Emotion Recognition using physiological data, Experiment with sensors

5

6

S5

Somnox Sleep Robot, Synchronisation of user’s breathing with the pillow

8

9

S6

Breathing and Better Decisions

10

140

S7

Managing attention via interface

11

142

S8

Capturing nuanced human preferences

12

S9

Colors and affect

14

S10

Deploying at Scale, Training with Individual feedback

15

16

168

S11

Finding content with interface design, Gut-based decision making

17

18

174

S12

Data-driven approach to match movies and colours

19

200

S13

Sensing Touch

20

206

82

2

13

7

120

138

156 164

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Physical Interface Prototyping

HYPOTHESIS 1

HYPOTHESIS 2

The pillow should feel inviting enough for the user to be motivated to touch it

The pillow feels comfortable and huggable during interaction

METHODOLOGY

METHODOLOGY

benchmarking, prototyping and user-feedback

precedent study, ergonomic study, rapid prototyping, user-feedback

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The design opportunity of the physical interface :

1

It should feel inviting

Prototype a unique form and complementing Colour, materials and finishes.

2

It should be huggable, playful & interactive

Study the ergonomics of pillows user interact with on a daily basis

Process has been shared within this section

DESIGN FOR BETTER DECISIONS

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The act of Hugging Boosts

Lowers

Some researchers believe that hugging and other interpersonal touch can boost a feel-good (also known as “the cuddle chemical”) hormone called oxytocin4 and modulate the endogenous opioid system (neurons in the brain that can produce soothing chemicals), both of which can boost health.

It also can lower blood pressure and lower levels of the “stress hormone” cortisol5

Oxytocin

Cortisol

Michael Murphy, Ph.D associate at the Lab for the Study of Stress, Immunity, and Disease in the Department of Psychology at CMU, says “Touch deactivates the part of the brain that responds to threats, and in turn fewer hormones are released to signal a stress response, and your cardiovascular system experiences less stress.”

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Listing keywords First, since the product had to feel inviting to the user, it was a given that it should stand out from the rest of its contemporaries (pillows / cushions) sitting next to it on the furniture. It should be intriguing.

Second, the pillow was supposed to be hug at the end of the day when the user usually feels tired, hence the form should seem snuggable.

The function of the pillow is to comfort the user, hence it needs to be warm and comforting.

Third, the user should interact with the pillow for various functions while taking a decision. So a play of textures was necessary in the physical interface.

A few words were listed to guide the process of arriving to the form and choose the colour, material and finishes.

DESIGN FOR BETTER DECISIONS

APPROACHABLE WARM INTRIGUING TACTILE SNUGGABLE PLAYFUL SOFT Moodboard was compiled to aid the process

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Various references ranging from movies to daily objects were compiled based on the keywords to aid exploration.

APPROACHABLE

PLAYFUL

TACTILE

FIG 8.17 MOODBOARD FOR PHYSICAL INTERFACE

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INTRIGUING

SNUGGABLE WARM DESIGN FOR BETTER DECISIONS

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Existing forms of pillows were used and verbal feedback was taken from a potential user. Posture, along with pillow type and the pain-points are shown.

To ensure a comfortable experience, the product was designed while keeping ergonomics in mind.

After this exercise, an ideal size of a pillow was derived that could be used to start building the form. It’s dimensions were supposed to be 18”height and 10” width.

FIG 8.18 EXISTING FORMS PRECEDENT STUDY WITH USER

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USER INTERACTION RELATIVE PILLOW SIZE

To understand the optimum dimensions and form of the product, available pillows of different shapes and sizes were tested with the user and the feedback was noted.

WHAT DOESN’T WORK

USER FEEDBACK

Precedent study of forms

“height not enough to rest my chin on Uncomfortable as I want to cross my arms but unable to due to large width”

“armrest is hard which makes it difficult to hug, can’t rest chin & shoulders are hurting”

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WHAT WORKS

medium size pillow held upright soft, pressing it with chin, able to rest neck after a long day

DESIGN FOR BETTER DECISIONS

Max size

Long narrow pillow

Bolster

wide and soft enough that it can be bent

“Change my body posture, can support my forearms for a long time otherwise hanging in the air”

Change my body posture, can support my forearms for a long time otherwise hanging in the air

thin, playful, ability to hold in different ways

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Form development ( Exploratory )

FIG 8.19 FORM DEVELOPMENT (EXPLORATORY)

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Form development ( Functional )

FIG 8.20 FORM DEVELOPMENT (FUNCTIONAL)

DESIGN FOR BETTER DECISIONS

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Tinkering with Materials

Different sizes of foam were tinkered with to understand how the comfort could be increased and the user-experience could become more delightful. An important insight from the random tinkering was the realisation of a painpoint. When a user holds the pillow for a long period of time, they can’t maintain the same posture for long since it starts hurting. After this armrests were introduced in the design.

FIG 8.21, 8.22 TINKERING WITH MATERIALS - FRONT VIEW & LEFT-VIEW

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FIG 8.23 FORM ITERATION POST TINKERING

DESIGN FOR BETTER DECISIONS

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Full scale rapid prototyping EPE foam was carved to develop the full-scale mockup after sketching ( Fig 8.24 ). Sketching was helpful to a calculative idea of the best snuggable form in terms of volumes and proportions. The developed prototype’s front and side views are shown.

FIG 8.24 ROUGH SKETCHING TO GUIDE SCULPTING

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The carving process is very intuitive in nature as it helps in understand the form in reality, involves more senses rather than in sketching on a paper format.

Based on user’s feedback during the process, a protuding form was added at the center bottom to help the form stay when held between the legs.

FIG 8.25 SCULPTED FORM

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Evaluation The goal was to test the assumptions placed while starting prototyping - mainly, comfort of the existing prototype. Also, to find an ideal position for the trackpad that will be used as an interface in the product to interact with the digital ecosystem.

Feedback (1) The pillow is comfortable to hug, as answered by the user while interacting as seen in all images (Fig 8.26,27,28). The hand rest is helpful even if the user inclines back at the backrest.

(2) Position of the trackpad to operate - ideal position is towards the center bottom (Fig 8.29).

The user will have to pivot their hands the least as compared to other possible trackpad locations.

FIG 8.26, 8.27, 8.28, 8.29 MULTIPLE VIEWS OF EVALUATION & USER FEEDBACK

DESIGN FOR BETTER DECISIONS

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Human factors & Ergonomics

EOD Buddy is a phygital device comforting the user at the end of the day. With comfort at it’s core, its interaction with human body is utmost critical. Certain aspects of the body that cannot be overlooked are : 1. Cervical (Trunk) - Vertical distance from sitting surface 2. Lower Lumbar - Vertical distance from sitting surface 3. Elbow to Elbow distance (relaxed) 4. Elows Flexed diameter 5. Hand breadth 6. Finger-tip breadth

DISCLAIMER : According to the book, the users belonged to the 95th percentile female and male Indian group.

So the two extremes of the range considered are 95th percentile and maximum measurements. This gave an idea, for the optimal measurements for the product.

If the product has to be released, then it should be produced in 2 sizes. One designed for the population higher than 50th percentile and another for 50th percentile and lower.

The prototype has been developed with the direct anthropometric data from Indian anthropometric dimensions for Ergonomic design practice by Debkumar Chakrabarti.

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(1) Pillow Height

C = A - B = 667 - 159 = 508 mm 508 mm (including memory foam layer of 25mm on both sides) makes it compressible by 50 mm when the user leans backward / forward / hunches.

B

A

C

FIG 8.30 DIMENSIONAL ATTRIBUTES OF PRODUCT IN COMPARISON TO HUMAN BODY

R.No.

Parameters

FIG 8.31 PROTOTYPE IN ACTION

Min

Percentiles

Max

5th

25th

50th

75th

95th

Mean

±SD

A

Cervical (Trunk)

489

531

582

605

634

667

887

607

46

B

Lower Lumbar

48

72

86

100

119

159

256

107

29

C

Pillow Height (difference)

505

515

508

631

500

FIG 8.32 TABLE DENOTING HUMAN & PRODUCT DIMENSIONS

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(2) Pillow Breadth & Armrest

Pillow breadth (PB) = 700 mm > D

Pillow Inner breadth = PB - (>F) = 400 mm

Elbow to Elbow (relaxed) distance for a relaxed posture for a long period of time (typically 2-3 hrs)

For a snug-fit and some room-to-play available for arms (>F), pillow’s Inner breadth is determined by the formula stated above.

>F

>F

E

>F = 150 G = 400 >D = 700 G Pillow breadth

D FIG 8.33 DIMENSIONAL ATTRIBUTES OF PRODUCT IN COMPARISON TO HUMAN BODY

R.No.

Parameters

FIG 8.34 PROTOTYPE IN ACTION

Min

Percentiles 5th

25th

50th

75th

95th

Max

Mean

±SD

D

Elbow to Elbow (relaxed)

330

389

451

494

539

632

821

501

52

E

Elbow flexed

162

231

255

273

293

331

367

276

30

F

Elbow Diameter

52

74

81

87

93

105

105

88

226

158

289

320

353

422

611

325

(Elbow flexed ÷ 3.14) G

Pillow Inner Width

FIG 8.35 TABLE DENOTING HUMAN & PRODUCT DIMENSIONS

(PB - 2F)

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(3) Pocket opening Max hand breadth with thumb (combined) is 117mm. To easily slide hands in the pocket, he unstitched pocket opening is (>H) 175mm.

H FIG 8.36 HAND BREADTH WITH THUMB

FIG 8.37 PROTOTYPE IN ACTION

(4) Chin indentation Neck breadth = I = 125 mm (combined), 105 mm (Females), 128 mm (Males) Chin indentation in form = 175 mm ( > I for comfortable support for chin for all genders) I DESIGN FOR BETTER DECISIONS

I<

FIG 8.38 NECK BREADTH

FIG 8.39 PROTOTYPE IN ACTION

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1:1 Object tracing ( Method 1 ) At first, full scale tracing of the 1st mockup was tried. While it was relatively easier (not really) to map the orthographic views, the method failed to understand the geometry of a parametric surface.

FIG 8.40 MANUAL OBJECT TRACING

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Photogrammetry ( Method 2 ) 3D Scanning was another feasible option to measure depth and recreate the mostt accurate model.

The physical prototype was 3D scanned and then an overlay was computationally modelled to soften the form mesh, enhannce the smoothness and slice it down to develop an iteration of the first mockup.

The new mesh created was mirrored so a symmetrical form could be achieved which will optimise the manufacturing process.

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FIG 8.41 MOCKUP SCANNING PROCESS FIG 8.42 SCANNED MODEL FIG 8.43 SMOOTHEN MESH LAYER OVER SCANNED MODEL

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Slicing

Stacking

Putty layer

FIG 8.45

FIG 8.46

The cross-sections were printtetd 1:1 and pasted on polytstyrene foam. The foam was cut along the cross-sections and stacked together using a metal rod and sanded to achieve a continuous flowy form

The form was covered with puttty before foaming.

The adhesive used would eat up polytstyrene foam hence it was necessary to cover it with 2-3 layers of putty.

Almost 2 kgs of Putty was used and the V2 Mockup became as much heavier that the final proposed prototype. The proposed mateiral in moulded plastic

FIG 8.44

To develop an iterated version of mockup, the iterated mesh 3D model was sliced into the available thickness of Polystyrene foam.

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FIG 8.44 SLICED ISOMETRIC VIEW FIG 8.45 SECTIONS STACKED TOGETHER FIG 8.46 A LAYER OF PUTTY APPLIED OVER SANDED FORM

102

Foaming

Surface development

Fabric covering

FIG 8.47

FIG 8.48

FIG 8.50

A layer of 25 mm Memory foam was layered over the mockup using a single piece. One could start feeling the comfort right away after this process.

A layer of 50 mm Memory foam is proposed for the final prototype.

Finally, the mockup was covered with the closest material possible to Stretchable Velvet. Evenly cut and shredded and finely woven silk-fibre velvet was used. This selection was intentional, since the fabric had tot be dyed latter.

Surface development was done along with a tailor to cover the prototype. For this activity, a layer of thin draping fabric was used before using the final proposed fabric. Here, the position of the click wheel was also finalised (paper at center bottom) FIG 8.49

FIG 8.47 FIG 8.48 FIG 8.49 FIG 8.50

DESIGN FOR BETTER DECISIONS

PROTOTYPE AFTER FOAM LAYER SURFACE DEVELOPMENT BY DRAPING FINAL FIT WITH DRAPING FABRIC PILLOW COVERED WITH UNDYED COVER

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Design for Affordance ( pocket ) How user will get a clue on how to interact with the pillow. A few designs were sketched and the most suitable was developed.

FIG 8.51

No. Hides the concave form developed to rest the arms.

FIG 8.53

No. The corners are even sharper than Option 2.

Emboidered shapes are playful though.

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FIG 8.52

No. Complements the form of the main body however has corners in it.

FIG 8.54

Yes. The corners are even sharper than Option 2.

Emboidered shapes are playful though.

104

Dyeing

Playful texture

Surface

FIG 8.55

FIG 8.56

FIG 8.57

The velvet was dyed in an ombre colour palette in a vertically symmetric pattern. Synthetic dyes deephues were used witht tie-dye technique.

Playful texture (eggcrate foam) was stitched within the pocket. With the eggcrate foam, playfulness became a synonym. One can keep fondling with the peaks and valleys within the foam, and the texture adds to the feel.

Using a white anchor thread, a stitched effect was given. Brass tacks were used at both the ends of the pocket. To depict the opening of pocket, stitched effect was intentionally avoided over those 7” of fabric.

Stark coloured darts have been intentionally avoided in the prototype with velvet due to its visual stress.

FIG 8.55 DYED FABRIC FIG 8.56 EGGCRATE FOAM PLACED IN POCKET FIG 8.57 SURFACE TREAMENT WITH STITCHED EFFECT

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WARM & EXCITING Felt warm and playful.

Brigh colours felt energetic & vibrant witht higher saturation

ICY & COLD The colour palette felt cool however dull FIG 8.58 DYEING SAMPLES WITH TWO COLOUR PALETTES

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Colours

Two colour palettes were initially compared using A-B testing on velvet. The interplay of colours with velvet created a shimmer on the fabric that was intriguing to touch.

The WARM & EXCITING THEME was chosen because they brought ou an element of play.

However, cool blue was replaced with a colour in the red family to add to the energtic feel. Higher saturation, deep hues were used to amp the energy.

PANTONE

PANTONE

PANTONE

17-5937 TCX Deep Mint

3591 C

18-1760 TCX Barberry

FIG 8.59

`

FIG 8.59 WARM & EXCITING THEME FINAL COLOUR PALETTE

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CMF Contrasting highly saturated deep hues amp the energetic feel of the pillow. The colour palette adds to the intrigue in a user’s mind, along with increasing the approachability and playfulness.

APPROACHABLE WARM INTRIGUING TACTILE SNUGGABLE PLAYFUL SOFT

Materials like foam was primarily used as comfort is the ultimate goal along with tech functionality.

With the eggcrate foam, playfulness became a synonym. One can keep fondling with the peaks and valleys within the foam, and the texture adds to the feel.

Memory foam makes the pillow soft and snuggable. Evenly cut & shredded, finely woven velvet amps the rejuvenating experience when the user actively interacts with the surface.

With the overall CMF strategy, a unique combination emerges which helps the user rejuvenate in a playful way.

FIG 8.60 PROTOTYPE WITH FINISHED SURFACE FIG 8.61 MATERIAL BOARD

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1

2

3

6

PANTONE

PANTONE

PANTONE

3591 C

18-1760 TCX Barberry

17-5937 TCX Deep Mint

5 1

Eggcrate foam

4

Memoy foam

2

Velvet

5

Colour strip

3

Anchor thread

6

Enamel finished tacks

4

DESIGN FOR BETTER DECISIONS

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DEVELOPMENT PROCESS OVERVIEW

1

Sketching

2

EPE Foam mockup

3

Photogrammetry (3D Model)

4

Iterated mesh (3D Model)

5

Slicing & Stacking

6

Putty layer

7

Foaming

8

Surface development

9

Fabric covering * This process was only used for developing the mockup.

These are not the final proposed materials.

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FIG 8.62 STEP-BY-STEP PROTOTYPING PROCESS

110

1

2

DESIGN FOR BETTER DECISIONS

3

4

5

6

7

8

9

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Proposed Material Palette

FIG 8.63

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Rear fabric flap Memory foam Plastic encasing Plastic encasing Memory foam

Electronics

Lower fabric flap Scroll Wheel Eggcrate foam Sensors Front fabric flap Pocket flap DESIGN FOR BETTER DECISIONS

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FIG 8.64 PROTOTYPE ALONG WITH OTHER CUSHIONS ON COUCH

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DESIGN FOR BETTER DECISIONS

CONCLUSION 1

CONCLUSION 2

Hypothesis is true. The form of the pillow comfortable and huggable.

However, iterations are expected since it’s yet to be validated while a large group of users

The pillow’s unique combination of color, material and finish invites different users to touch and explore the pillow. However, iterations are expected since it’s yet to be validated with large group of users

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Automatic detection of hand

HYPOTHESIS 3 The presence of a hand can be detected in a pocket

METHODOLOGY p r o t o t y p i n g & t e st i n g

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For a seamless user experience, as soon as the user puts their hand in the pocket, the digital interface should switch on removing the redundancy to do it manually.

Detecting presence with GSR Sensor The GSR sensor contains two electrodes. These two electrondes are constantly measuring conductance and the resistance. These sensors are placed in the pillow where the wrist will be located within the pocket.

The electrodes recognise the conductance (skin) when the skin comes in contact with them and transfer these signals to the micro-controller. So the micro-controller would know when the hand isn’t in touch with the electrodes and can detect the presence of the hand, and consequently turn on the device used to watch the movie.

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CONCLUSION 3 A successful low-fidelity prototype of a functional capacitive sensor depicts that the hypothesis is true.

FIG 8.65 USER-INTERACTION WHILE DETECTING FIG 8.66 SENSOR USED WHILE EXPERIMENTING FIG 8.67 EMBEDDED SENSORS TO BE USED IN FINAL PROPOSED PROTOTYPE

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Content preference according to current emotional state

HYPOTHESIS 4

According to the secondary research during the Synthesis phase, a few anecdotes from the users were found to support that they were unable to find something to watch. They are unhappy with the current recommendations by the OTT platforms.

User’s current affective state is a factor in determining what they want to watch

Essentially, emotional states play a role while taking a (entertainment) decision. Give you a sense of what / how they want to choose.

“I felt low after scoring grades in a test. I wanted to feel motivated, and understand that this is not the end of the world!”

METHODOLOGY Literature review, precedent study, interviews

- an interviewee while describing how she constructs her movie choices

FIG 8.68 DISCONTENTMENT OF USERS UNABLE TO FIND CONTENT

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The OTT platform are constantly trying for new ways to recommend, and one of their ways of offering their users a better experience is by suggesting content by their mood.

CONCLUSION 4

The OTT platforms have recognised that it is one of the factors that will solve the problem.

Hypothesis is true

FIG 8.69 PRIME VIDEOS RECOMMENDING CONTENT BASED ON EMOJIS

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Physiological Indicators and Data Collection

HYPOTHESIS 5 It is possible to record and extrack physiological data in real time while sitting

METHODOLOGY literature review, precedent study

FIG 8.70 TRACKING DEVICES USED TO CAPTURE CURRENT STATE OF USERS

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A lot of progress has been achieved in human behaviour research through data collection with multiple biosensors (images).

The increasing fidelity and mobility of biosensors coupled with the online data collection capability has made behaviour research even more convenient.

Consumer-electronic and fitness companies have launched products like Apple Watch, Fitbit bands and other smaller players in the market have been made physiological-data measurement and tracking mobile and on-the-go.

CONCLUSION 5 The hypothesis is true. It’s possible to record and extract physiological data in real time while sitting.

One can measure their blood oxygen level with revolutionary sensors and apps. Take an ECG anytime, anywhere. See their fitness metrics at a glance.

FIG 8.71 MONITOR HEART RATE WITH APPLE WATCH

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Choosing the right datapoint to detect emotion Summary HYPOTHESIS 6 A user’s current emotional state can be elicited using physiological data

There’s a reliable coorelation between user’s current emotional state and the physiological data. This sub-section shows the process with which the right datapoint was selected to detect emotion during the final experience.

• Rapid increase in emotionrecognition research

• Emotion recognition methods and ‘why not’ certain methods work

• Observations from summary of previous research

METHODOLOGY literature review, counceptualising

MANAN PAHWA • NATIONAL INSTITUTE OF DESIGN

Rapid increase in emotion-recognition research

The past five to ten years has seen a rapid increase in emotion-recognition research via physiological signal detection in multiple fields like healthcare, consumer healthcare, consumer packaged goods and academia. Various methods have evolved and will continue to develop.

122

Emotion recognition methods

FIG.8.72 CLASSIFICATION OF MEASUREMENT METHODS FOR EMOTIONS RECOGNITION

A list of why not certain methods works A method based on conscious response?

The user-experience subconsciously positions the user in a calming rate. During this activity, the data is collected in the background with ethical consent. Hence, data collection methods should be unscious / non-invasive in nature. The goal is to give a non-invasive user-experience.

Methods based on direct sensors?

Though direct sensors have evolved a lot from requiring large setups to wireless sensors, however still require a lot of cumbersome equipment which increases the probability of user becoming conscious and gathering noise in readings.

Methods based on non-contact measurements?

Non-contact measurement methods like remote PPG are far from evolving to a stage to gather physiological data without noise.

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A combination of both electrical and non-electrical parameters was used to cross-validate the data and cancel the noise received.

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FIG.8.73 SUMMARY OF PREVIOUS RESEARCH WITHIN THE SPACE OF EMOTION RECOGNITION USING PHYSIOLOGICAL SIGNALS.

SUMMARY The comparation of recognition rate among previous research. This paper6 amalgamates several (20-30) previous research done into emotion recognition using multiple methods HR, EEG, ECG, eyes, body posture and puts its learnings together in tabular formats.

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OBSERVATIONS - Wherever GSR is used (in addition with other indicators) as an indicator ( 25, 23, 21, 9 ) the recognition rates have been higher.

- Out of 27, 12 experiments have used ECG as an indicator which implies that ECG is a reliable indicator across practitioners.

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Coorelation between emotions and physiological signals

There is a reliable coorelation between user’s current affective state and physiological signals in studies conducted.

1

2

Skin temperature

HR & HRV

Finger temperature varies due to emotional states and applied stimulus. Emotional stress like anxiety or hostility causes decrease of finger temperature.

HR =

Number of heartbeats per minute.

HRV = Heart rate variability is literally the variance in time between the beats of your heart.8

Based on previous research’s efficacy and the nature of emotion-detection to be non-invasive, a combination of 4 data-points was chosen. Each data-point has been expanded with their basic definition, their effectiveness, their relation to emotion and their capability of detecting.

relation to emotion Anger is characterized by disordered heart rhythm pattern and increasing heart rate. In contrast, relaxation produces lower amplitude heart rhythm. Figure on the right explains the visual correlation between heart rate and emotions.7 HRV is indicative of whether the body is in an active state and is capable of adapting to sudden changes or not.

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FIG 8.75 GRAPHIC DEPICTION OF CALCULATION OF HRV. THE APLITUDES REPRESENT PULSE

FIG.8.74 GRAPHIC DEPICTION OF EVERYDAY STATES AND HYPER-STATES OF PSYCHOPHYSIOLOGICAL INTERACTION DISTINGUISHED BY HRV TYPOLOGY

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3

Respiration rate RR defines the characteristic of respiration over time. Respiratory monitoring data contains useful information about emotional states. Respiration velocity and depth usually vary with human emotion.

relation to emotion Deep and fast breathing shows excitement that is accompanied by happy, angry, or afraid emotions; while relaxed people often have deep and slow breathing and so on.9

FIG 8.75 RECORDINGS OF PROTOTYPICAL BREATHING PATTERNS FOR EACH BASIC EMOTION.

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4

Galvanic Skin Response (GSR) The GSR signal represents the electrical conductance of the skin. The change in conductance of the skin is an involuntary response to emotional arousal.

relation to emotion Fear, anger, startled response, orienting response, and sexual feelings are among the reactions that may be reflected in EDA.10 These responses are utilized as part of the polygraph or lie detector test.

DESIGN FOR BETTER DECISIONS

FIG 8.76 GSR DETETCTING AROUSAL AT SPECIFIC POINTS WHILE USER WATCHES A VIDEO

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Raw signal

Preprocessed

Data points

Trained Baseline data

Feature

Data points

GSR

GSR

Trained Baseline HRV data

Leading HRV

RR

metric

Leading metric

RR

SKT

SKT (Collection of data through sensors)

(Collection of data through sensors)

Deconstructed Deconstructed Dominant Emotion Dominant Emotion

Feedback loop

Feedback loop

Color psychology rule engine

Color psychology rule engine

Emotional Colour

Emotional Colour

GSR = Galvanic Skin Response HRV = Heart Rate Variability RR = Respiration Rate SKT = Skin Temperature

GSR = Galvanic Skin Response HRV =INSTITUTE Heart Rate Variability MANAN PAHWA • NATIONAL OF DESIGN RR = Respiration Rate

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Emotion recognition algorithm In order to elicit emotions from physiological signals, a hybrid model is proposed using sensor fusion and emotion-sensing technology to derive how you’re feeling in the moment, to predict the right choice of movies the viewer be inclined towards watching.

Biosensors would be used to extract raw data which will be preprocessed and features would be extracted from the received data. Before this process, the algorithm will be trained with training datasets for a period of time. These datapoints will converge into a leading metric to deconstruct the dominant emotion.

CONCLUSION 6 There’s been reliable co-orelation between user’s current affective state and physiological signals combined.

Theory of an emotion-recognition algorithm is in place, however needs to be validated further.

The dominant emotion will become an input to the CPRE (Color psychology rule engine) and the screen would display an emotional colour.

FIG 8.77 EMOTITON RECOGNITION ALGORITHM CHART

DESIGN FOR BETTER DECISIONS

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Experiments with Sensors GOAL

HYPOTHESIS 7 These data-points are reliable indicators of the current affective state of the user (a) individually (b) in combination

Emotion-detection is a complex multi-modal task which is easier in principle however difficult in execution, having multiple limitation points.

METHODOLOGY

In order to test the effectiveness of the emotionrecognition algorithm, a primary experiment was performed with the following objectives :

• To create a low fidelity working prototype of the emotion recognition algorithm.

• Experiments were performed to understand the tech better and to cross-check and validate the effectiveness of the sensors (at a preliminary level) as seen in secondary research.

• The goal was not to detect accurate figures, but to just test whether these activities (and emotions) will generate a deviation from the baseline reading

literature review, prototypting and usability-testing

FIG 8.78 ACTIVITY WHILE RECORDING DATA

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SETUP The experiments were performed with Amogh Jadhav, product design student at the National Institute of Design. The experiments were planned, setup and guided by Manan Pahwa while setting up the sensors, gathering and recording data and reducing noise were performed by Amogh. These experiments were performed while in completely remote settings.

A combination of 3 sensors was setup with an Arduino. Due to resource limitation, less sensitive sensors were used. These sensors are just used to perform preliminary experiments, and not for final design proposal. SKT was not measured because of resource limitation.

3 sensors : • PPG sensor to derive HR & HRV • Gyropscope & accelerometer for measuring the expansion and contraction of the abdomen in coherence with the breathing rhythm • GSR sensor to detect arousal

DESIGN FOR BETTER DECISIONS

FIG 8.79

FIG 8.80

FIG 8.81

FIG 8.79 GSR SENSOR FIG 8.80 RESPIRATION SENSOR (GYROSCOPE ON).

FIG 8.81 PPG SENSOR WITH ARDUNIO UNO

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FIG 8.82 TABLE OF OBSERVATIONS AND INFERENCES DERIVED FROM EXPERIMENTS

Baseline

Hypotheses on features of data

The user is calm at the starting of the experiment

HRV (in RMSSD)

152.90

Observation Value is lower than baseline

Watching an exciting video

Box breathing

Box breathing is supposed to calm down and balance the ANS. HRV

Ideally, lower than baseline

HR

Graph will be flatter

RR

Smooth curve and long gap between 2 consecutive valleys/peaks. Breathing will be deep and slow.

45.16

Observation Value is higher than the baseline

Exciting video is supposed to show occasional emotional around and balance the ANS. HRV

Will show relatively higher values

HR

A general rise with occasional peaks & valley.

RR

The difference between consecutive peaks and valleys will be less. Breathing will be deep and fast.

151.60

Jumping jacks

High becauses of extremely active nature of sympathetic nervous system

HR

Emotional state of the user is excited and happy. So, irregular & drastic peaks/valleys should be observed.

RR

Consecutive peaks/valleys difference will be low while amplitude will be high. Breathing will be deep and fasted than observed after watching an exciting video.

MANAN PAHWA • NATIONAL INSTITUTE OF DESIGN

Inference The sympathetic system becomes active due to emotional araousal and introduces variation in heart rate

Observation Value is much higher than the baseline

Jumping jacks is supposed to activate the body.

HRV

Inference The parasympathetic system is dominant and is resulting in less varation of heart rate.

219.81

Inference The sympathetic system is dominant and is opposing the parasympathetic system.

Show very high variation in heart rate.

134

BPM vs Time (300 sec)

Expansion and contraction (Respiration)

Observation shape of the graph is flatter as compared to the baseline

Observation1 shape of the curve is softer Inference1 box breathing is supposed to calm down

Inference box breathing activates the parasympathetic nervous system

Observation2 interval between consecutive expansion is long Inference2 user feels relaxed after box breathing

Observation shape of the graph shows fluctuation with peaks and valleys

Observation shape of the graph is flat and valleys are short

Inference Watching exciting videos stimulates emotions and brings more fluctuations in heart rate

Inference Respiration velocity is higher and breathing depth is low

Observation shape of the graph shows fluctuation with drastic peaks and drastic valleys

Observation1 peaks and valleys are sharper Inference1 Breathing after high arousal and positive valence shows frantic respiration on movement.

Inference physical exercise tends to pump up the heart and shows characteristics of high arousal and positive valence.

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Observation2 peaks and valleys are closer Inference1 Breathing after high arousal and positive valence shows more rapid breathing in and breathing out, and less holding of the breath.

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Limitations of experiment

As mentioned earlier, due to resource limitation less sensitive sensors were used. SKT was not used because of resource limitation.

Even slight disturbances when measuring cause extraneous values. The user have to keep my hand very still. Even though there is a difference between the sensors used for the sake of the experiment and the proposed sensors, the proposed sensors (used in iWatch) requires the user to hold the watch in a stable position in order to get an effective reading.

The users should not be able to detect what we’re tracking, since respiration is a voluntary action the user can consciously change their breathing rhythm. This would add noise to the signal. The digital signal should be captivating enough for the user to breathe and in a unconscious manner.

During the experiment, the data collected was for a minimum 5 min. With more development, we want to reduce the time of data collection to 1 minute.

HRV is an extremely sensitive metric. Results might vary in the final condition.

Due to technological limitation, the studies were backed by a low-fidelity prototype in a particular setting and limited to a single person for the sake of the experiment.

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CONCLUSION 7 Hypothesis is partially true.

The experiment concludes the potential however needs to be validated further.

DESIGN FOR BETTER DECISIONS

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Somnox Sleep Robot

HYPOTHESIS 8 The pillow can adjust its breathing according to the user

METHODOLOGY Literature review, benchmarking

MANAN PAHWA • NATIONAL INSTITUTE OF DESIGN

FIG 8.83 GRADUAL SLOWING DOWN OF BREATTHING FREQUENCY OF THE USER

With the “adaptive breathing” feature, the Sleep Robot automatically adapts to the breathing frequency of the user. The breathing frequency is measured by a motion sensor.

The Sleep Robot adapts to the user’s breathing and will gradually slow down, while constantly measuring the breathing frequency.11

CONCLUSION 8 Such technology exists that can detect user’s breathing and adjust its breathing according to the user and gradually slow down the breathing rhythm to a calming rate.

138

Synchronisation of user’s breathing with the pillow

HYPOTHESIS 9 Users will synchronize their breathing to the pillow’s rhythm

The concept of synchronization of breathing patterns is based on a scientific study in which premature infants slept with a breathing bear.

During sleep, infants usually have chaotic breathing, which can result in poor sleep. By using the bear, infants adapted to the regular breathing pattern of the bear resulting in an improved sleep quality12

FIG 8.84 USER’S BREATHING SYNCHRONISATION WITH SOMNOX SLEEP ROBOT

Success story

METHODOLOGY Literature review

Somnox’s functioning is also based on this study.

Two-thirds of the test users experienced their breathing rate slowing down when using the Sleep Robot. In general, the breathing movement was experienced as a realistic simulation.

However, the same technology needs to be tested in this context which will be a part of the future scope.

DESIGN FOR BETTER DECISIONS

CONCLUSION 9 The hypothesis is true. A study shows a positive coorelation between user’s breathing rhythm and an object

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Breathing & Better Decisions BENEFITS

Box breathing, also known as square breathing, is a technique used when taking slow, deep breaths.

HYPOTHESIS 10 Breathing helps in rejuvenating cognitive resources and enhancing decision-making capability

According to the Mayo Clinic *. there’s sufficient evidence that intentional deep breathing can actually calm and regulate the autonomic nervous system (ANS)13

Box breathing can reduce stress and improve your mood. That makes it an exceptional treatment for conditions such as generalized anxiety disorder (GAD), panic disorder, post-traumatic stress disorder (PTSD), depression and insomnia.14

This system (ANS) regulates involuntary body functions such as temperature. It can lower blood pressure and provide an almost immediate sense of calm.

METHODOLOGY Literature review

* Mayo Clinic is a nonprofit American academic medical center focused on integrated health care, education, and research.

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SCIENTIFIC EVIDENCE

How breathing can help you make better decisions: Two studies on the effects of breathing patterns on heart rate variability and decision-making in business cases15 In Study 2, 56 students were randomized to perform 2 min of the skewed vagal breathing** (experimental group) or to wait for 2 min (controls), before performing a 30-minute business challenging decision-making task with multiple choice answers.

**Vagal breathing is slowed down respiration cycle with longer exhales. The [vagus] nerve, as a proponent of the parasympathetic nervous system (PNS), is the prime candi-

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Stress levels were self-reported before and after the task. While controls reported elevations in stress levels, those in the experimental group did not. Importantly, participants in the experimental group provided a significantly higher percentage of correct answers than controls.

These studies show that brief vagal breathing patterns reliably increase HRV and improve decision-making.

CONCLUSION 10 The hypothesis stands true.

Studies show the effects of breathing patterns on heart rate variability and decision-making in business cases.

date in explaining the effects of contemplative practices on health, mental health and cognition.

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Managing attention via interface

HYPOTHESIS 11 Visual experience will (i) position the user in subconscious calming level and (ii) keep them engaged

The user journey was implemented first in a textual format. It was later implemented visually; the whole framing process helped in visualising at both macro- and micro-level of digital userexperience. Inspiration boards and storyboards were created, followed by explorations and final UI screens.

Though the digital experience was designed together end-to-end, it has been divided further to clearly communicate how each hypothesis was addressed.

METHODOLOGY Prototyping

FIG 8.85 STORYBOARDING PROCESS FOR DIGITAL EXPERIENCE

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Goal

Framing & Conceptualisation

1

The goal of first two phases (Approach, Rejuvenate) of the digital-experience was to help the user transition into a different world and make the user feel rejuvenated and the third phase was to make them play.

POSITION IN A SUB-CONSCIOUS LEVEL

The users start experiencing while they are in a fatigued affective state - the user experience should be a rejuvenating one.

2

KEEP THE USERS ENGAGED / MANAGE THEIR ATTENTION

Before subscribing to the service, the users would grant permission to record data which will be regulated under law. Once done, the user shouldn’t feel conscious that they are being tracked on an everyday basis.

Involuntary respiration data will be used to elicit the affective state of the user. If the user becomes conscious, the data recorded will include noise to which a solution is yet to be found. Hence, it is important for the UI to keep the user-engaged.

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Since the third phase was dependent on colour psychology and metaphorical meanings, the first phase was intentionally kept monochromatic and very clear. These visuals (shapes, movements etc) used to help users rejuvenate were backed by scientific evidence.

The UI was devoid of any destractions and purely focusing on the subject. This was done so the user’s brain can decloud; focus and subconsciously calm itself down. The goal was to make them clear-headed and recharged for another decision.

It is responsive to the signals their body innately is trasmitting without explicitly conveying it to the interface.

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The user interface for the first and second phase is divided into three parts viz :

I

II

III

MEDITATIVE FORMS The screen gradually lights up and positions the users in a subsconscious calming zone

DETECTING & GUIDED BREATHING Visuals guide the user to breathe calmly

EMOTIONAL COLOUR A relative colours of detected emotion unveils

FIG 8.88 CONCEPTUALISATION TO DERIVE DIGITAL UI ELEMENTS

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PART I MEDITATIVE FORMS

Storyboard ( Option 1 : Create ) Using pointillism as an inspiration, a white circle appears on a black screen as soon as the sensor detetcttst the hand in the pillow’s pocket. It follows user’s breathing.

FIG 8.89

User Interface

FIG 8.90

For final user-interface, renders are exported from Processing software. They are ye to be aligned with user’s breathing. Option 1 was dropped since the intetrface might make the user conscious if it follow’s their breathing pattern.

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Moodboard ( Option 2 : License )

User Interface Simple meditative forms to position in subconscious calming zone

Option 1

Simple meditative forms to position in subconscious calming zone

Fig. Collosal sphere made of points rotating along the vertical axis. The rotation speed is slow and meditative.

Option 2 Fig. Tentacles like structure rotating on vertical axis. Visual’s flow is calming and meditative.

FIG 8.92 OPTION 1 FIG 8.93 OPTION 2

FIG 8.91 MOODBOARD

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PART II DETECTING & GUIDED BREATHING

Inspiration board Inspiration : Kinetic art table that creates Sand Art. Metal ball creating intricate patterns on the table are calming and mesmerising. These were taken as inspiration for pattern and texture to develop the UI.

FIG 8.94 INSPIRATION ELEMENTS

Storyboard

FIG 8.95

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Spiral starts from the bottom and rotates clockwise. The upper half of the spiral is for the user to breathe in and the lower half is for breathing out. The spiral slows down at the equator level to transition the breathing.

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User Interface

FIG 8.96

FIG 8.97

Version 1 : At first, the spiral seems like a vertigo. It is not at all calming.

Version 2 : In this trial, the spiral was matched with chest expansion and contraction while breathing in and out respectively.

FIG 8.98

FIG 8.99

Version 3 : In this version, The spiral seems to leave a trail which is a print of a circle at each nanosecond. This pattern seems more calmer & intricate.

Version 4 : Negative of V3. With this choice of background, the transition from Part I will be easier.

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PART II DETECTING & GUIDED BREATHING

Inspiration board ( Iteration ) Spiral as a UI did not evoke conscious breathing as a heuristic. Hence, an iteration was needed.

The next option was to use a visual which literally relates with inflation and deflation.

FIG 8.100 INFLATE-DEFLATE MOVEMENT

Though the decision was to go for Deflate to Inflate state of a ball, Water ripple was used as an inspiration for its attribute to instill calmness.

FIG 8.101 WATER RIPPLE

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User Interface

FIG 8.102 FINAL UI FOR DETECTING & GUIDED BREATHING

A monochromatic ripple was prototyped. Figures show the state of inflating - trail is inside the circle (1-3) followed by deflating (4-6) - trail is outside the circle

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PART III EMOTIONAL COLOUR

Inspiration board

Storyboard

FIG 8.106

After the spiral covers all circles within the bounding screen, the node stops at the centre of another white background screen and a colour pops out which expands and covers the whole screen at first, later settles down at the bottom. This colour represents the current emotional state of the user.

FIG 8.103 IPHONE SCREEN FIG 8.104 WATERCOLOUR FLOW ON PAPER FIG 8.105 GALAXY IMAGE

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User Interface Option 1 Water colour effect - Multiple layers of different opacity were used to create watercolour effect.

FIG 8.107 OPTION 1 : WATER COLOUR EFFECT

Option 2 Flat UI - A clean flat element was developed with a base colour.

Both will be used and the final will be decided with usability testing.

FIG 8.108 OPTION 2 : FLAT UI

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Simple meditative forms to position in subconscious calming zone

I Meditative forms

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II Detected & Guided Breathing

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III Emotional Colour

CONCLUSION 11 Fluid animation and calming uncomplicated visuals are used.

The design intervention needs to be tested in order to validate the hypothesis further.

FIG 8.109 MEDITATIVE FORM UI FIG 8.110 DETECTING BREATHING UI FIG 8.111 EMOTIONAL COLOUR UI

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Capturing nuanced human preferences THE PROBLEM HYPOTHESIS 12

HYPOTHESIS 13

The set of algorithms will be able to identify user’s desired affective state & personalise recommendations

The feedback loop will help in personalising recommendations

For example : There is a difference between wanting to watch an uplifting hopeful movie and a sad wallowing movie after a break-up or a bereavement to ease into it.

METHODOLOGY

METHODOLOGY

Literature review, precedent study, conceptualisation

Literature review, precedent study, conceptualisation

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Humans are complex beings. Simply put, a difference can be observed when different people choose different ways of dealing with the same emotion.

During an informal talk, two users were asked what are they most likely to watch after a breakup in both the scenarios.

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Same Feeling, different content

1

Different users will have a different way of dealing with the same emotion and will prefer watching different content.

2

The same user while experiencing a certain emotion in two different situations will also be inclined towards watching a different type of content.

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When feeling like watching uplifting hopeful content

When feeling like wallowing and want something to ease into it

FIG 8.112 POSTERS OF MOVIES CHOSEN BY USER WHEN FEELING LIKE WATCHING HOPEFUL CONTENT

FIG 8.113 POSTERS OF MOVIES CHOSEN BY USER WHEN FEELING LIKE WALLOWING

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Personalisation of thumbnail artworks by Netflix

CASE STUDY

WHY - THE RATIONALE

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Netflix personalises the thumbnail artworks for content using AVA (Aesthetic Visual Analysis - set of tools and algorithms)

Netflix understands how do their customers pick what to watch? The list includes Catchy title, Interesting synopsis, Unwillingness of watching another episode of ‘The Office’ or maybe, a particular piece of cover art speaks to their spirits.

That’s why Netflix’s thumbnails are tailor-made.16

Humans are intensely visual creatures.

Our eyes move 3-4 times a second to process new information & NETFLIX’s goal is to get the user’s attention and hold it, the company puts a lot of work into choosing every thumbnail they’ll see.

FIG 8.114 DIFFERENT POSTERS BASED PERSONALISED TO EACH USER

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HOW DOES NETFLIX DO IT?

WHAT HAVE THEY ACHIEVED

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Step [1] : Frame annotation

Step [2] : Image ranking

Netflix annotates frames and creates metadata for each video frame based on 3 categories : Visual (brightness, color, contrast & motion blur), Contextual (documents face & object detection, motion, shot angles) and Compositions (focuses on visual principles in cinematography, photography & design like Depth of field, symmetry)

Netflix determines the shots that are most attractive and clickable, and aren’t blurry, have varied imagery, feature major characters and don’t contain sensitive or unauthorised branded content.

Netflix trains AVA overtime and learns overtime.

They are able to calculate regional differences (Germans like abstract posters, US population like posters with visible characters & clear-cut story plots) . Ofcourse these predictions are not always right but it’s getting there.

FIG 8.115 METADATA OF A FRAME FROM A MOVIE

Then they bring in the creative team to design thumbnail artworks which will later be used to personalise the experience of users.

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THE SOLUTION

Personalisation of content while learning from individual preferences.

Personalising content according to individual preferences

INCREASING LEVEL OF NUANCES

Individual level Community level General principle level

FIG 8.116 LEVELS OF PERSONALISATION

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The underlying principle An algorithm model is proposed - this algorithm will understand multiple desired emotional states of a user in a particular current emotional state (happy, sad, angry etc).

FIG 8.117 HUMANS IN THE NEGATIVE PASSIVE STATE (SAD) TEND TO MOVE IN THE POSITIVE PASSIVE / POSITIVE ACTIVE QUADRANT.

This model is based on the Pleasure-Pain* principle to predict the desired emotional state and the set of colours associated. * The Pleasure-Pain principle suggests that people make choices to avoid or decrease pain or make choices that create or increase pleasure. The pain pleasure principle is the core of all the decisions we make. Beliefs, values, actions and decisions are built upon this principle.

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FIG 8.118 HUMANS IN POSITIVE ACTIVE STATE (EXCITED) TEND TO MAINTAIN THE EMOTIONAL STATE IN THE SAME QUADRANT OR PACIFY IT.

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Training datasets

Testing datasets

Training with individual feedback

The Pain-pleasure principle can be a good starting point to base assumptions on user preferences of desired emotional state. Journalists and community of movie-buffs recommend movies based on how they make you feel and these unstructed dataset can be used to validate assumptions at first.

The trained algorithm will be tested with multiple focus-groups across diverse geographies, cultures and age groups.

With constant feedback by the user on the recommneded content by the algorithm, the recommendation engine will be successful in picking up nuances of individual preference over time. This feedback loop can be created with color psychology rule-engine.

With this, the content can be personalised at a community / culture level.

FIG 8.119 BEHAVIOUR-DRIVEN ALGORITHM

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CONCLUSION 12

CONCLUSION 13

Theory is in place however the set of algorithms need to be tested in order to validate the hypothesis.

Theory is in place however the set of algorithms need to be tested in order to validate the hypothesis.

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Colours and affect

HYPOTHESIS 14 Colours represent how we feel and be used as heuristics for content-choices

Colours represent how we feel. According to color theory, colours have the ability to evoke certain emotions and make us feel in a certain way. They have a psychological impact on us.

A study was conducted to understand the Effects of Color on the Moods of College Students.17 While the study established that the effect of colour on the mood, it also reiterated the following variables for a colour student to be sucessful.

METHODOLOGY Literature review

FIG 8.120 COLOUR AND ASSOCIATED TAGS INCLUDING MOOD STATES

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Colours as heuristics In Fig 8.36, (top) objects with colour red & orangeyellow remind of fruits - apples and mangoes, and (bottom) without colour don’t. This example tells how the brain uses colour as a heuristic to decide whether the object is familiar or not.

Experiment In this particular scenario, when the viewer is already tired, let’s see what the CPRE will prioritise.

Color theory says when a user is tired, the user usually wants something rejuvenating and refreshing. Hence, the CPRE will prioritise a stream of pastels, like Aqua which is rejuvenating and refreshing due to absence of red.

The user’s heart will suddenly tell them to choose it since they are most likely to be inclined towards it. There is no involvement of System 2 thinking here.

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It is important to reiterate that, for a colour study to be successful, confounding variables such as subjects’ age, gender, emotion, hue, brightness, saturation, light sources, adjacent colors, contexts, and cultural factors must be precisely controlled.

Based on the study, the goal was to understand what would be the confounding variables that should be accounted for the service to be successful.

Accounting and addressing these gaps will be essential while deploying the service at scale.

FIG 8.121 OBJECT RECOGNITION WITH COLOURS COURTESY : TONOY SARMA

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Colours in visual storytelling Colours are deliberately used individually & in interaction with each other, to accentuate a certain feel within a plot.

For eg. In Fig (1) bright and saturated colours suggest a happy tone , Fig (2) dark and desaturated suggest something more grounded

FIG 8.122 (1) BRIGHT AND SATURATED COLOURS SUGGEST A HAPPY TONE

FIG 8.123 (2) DARK AND DESATURATED SUGGEST SOMETHING MORE GROUNDED

Wes Anderson uses his color palette to split time periods in Grand Budapest. Each era’s saturated colors represent the mood at the time, specifically with regard to the hotel itself18 He’s also famous to merge the boundaries of the preset rules of colour to represent an emotion.

FIG 8.124 BLURRING THE TONES IN GRAND BUDAPEST BY WES ANDERSON

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David fincher deliberately and consistenly uses yellow and blue in his directed series - Mindhunter.

Both colors are near opposites on the color wheel, creating a certain contrast and tension between the scenes19. A blue colour alone would feel very cool and relaxing otherwise.

CONCLUSION 14 Each colour has the ability to evoke different emotions and various colors represent various moods.

FIG 8.125

GAP *The difference in preferences and perception are different for individuals and depend on a variety of factors. **Screen difference calibration for colours, for a colour study to be successful, confounding variables must be precisely controlled.

FIG 8.126 A SHOT FROM MINDHUNTER - DAVID FINCHER CREATES TENSION USING CONTRASTING COLOURS

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Deploying at scale THE PROBLEM

HYPOTHESIS 15 Each individual’s difference in preferences and perception of colour can be accounted for by the algorithm

METHODOLOGY Literature review, expert review

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An algorithm is proposed based on the conclusion that colour can be used as heuristics for decision-making with emotions.

While at an individual level this seems fine, what about scaling it to a large scale audience? It doesn’t work with óne size fits all’ - that’s what flickseeker has been trying to do..

A single color can have series of meanings and interpretations to various people in various regions of the world; take for example the people of China who see white as a sad color because they wear white when mourning whereas some other societies in Europe perceive it as purity, virginity, and cleanliness.

De Bortoli and Maroto (2001)20 also states that in Asia, orange is a positive, spiritually enlightened, and life-affirming color, whereas in the United States, it is a color of road hazards, traffic delays, and fast-food restaurants.

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Expert review An Expert review was conducted with Dr. Tim Holmes, UK based Independent Vision Neuroscientist on this topic.

“Flickseeker suffers from the regression to the mean problem of large datasets”

“Learn from the individual rather than learning from the masses to make recommendations” FIG 8.127 CONVERSATION WITH VISION NEUROSCIENTIST DR. TIM HOLMES

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Learn individual preferences in real time

“This is where biometric indicators of cognitive & emotional states come in. Because they are typically signals for the end-state rather than the parameters that get you to it.”, Dr Holmes states.

He supported this by an example of his PhD. He used eye-movement patterns - consistent across end-states to learn about individuals preferences & then optimise the design in real-time.

Usually, in sentiment-analysis tests, emojis (reduced icons for emotions) are used as stimuli to elicit emotion preference and respondents are asked to choose a preferred end-state (Fig 8.128). This direct input is validated with implicit inputs like eye-movement patterns, Galvanic Skin Response as these data points present the actual response of the respondent (Fig 8.129).

FIG 8.128 SENTIMENT ANALYSIS USING EMOJIS

There is room for ML experiments with choosing colours according to the feel with GSR sensors working in the background. The GSR sensor will detect the colours which emotionally arouse the viewer.

FIG 8.129 BIOSENSOR TO MEASURE GSR

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Learn from sub-conscious preference of a viewer

GSR Sensors can be used to gather the subconscious preferences of the viewer and learn from them. Raw GSR signal consists of two main components: SCL and SCR (Fig 8.131) Out of the two, SCR (Skin conductance response) is sensitive to specific emotionally arousing stimulus events. These bursts occur between 1-5 seconds after the onset of emotional stimuli.

The data in Fig.8.130 Below shows the GSR signal during a 20-minute video screening. The primary research question was: Which are emotionally arousing scenes in the video? Respondents were seated comfortably in front of the monitor with GSR sensors attached to the index and middle finger of the non-dominant hand. They also used a face camera to track facial expressions.

FIG 8.131 COMPONENTS OF GSR

FIG 8.130

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Similarly, the stimuli can be presented in a sequence (one after another) and the SCR can be mapped as an indicator of specific emotional arousal.

In this case, the stimuli would be colours (8explained further in 10.2). The design challenge is to design an interface that grabs the attention of the user to a single colour in the given moment to attribute the arousal to that particular stimuli. This can be used to better understand the difference between what user responds explicitly v/s how they feel implicitly.

IMPLICIT INPUT EXPLICIT INPUT

Embedded GSR sensor detects the implicit response

User selects the colour of choice by explicitly clicking on the scroll wheel

STIMULUS IN FOCUS PREVIOUS STIMULUS

ENTERING STIMULUS

CONCLUSION 15 Theory is in place however needs to be tested in order to validate the hypothesis.

FIG 8.132. PHYGITAL EXPERIENCE OF INPUTTING PREFERENCE EXPLICITLY ALONG WITH IMPLICIT DETECTION OF PREFERENCE. ON SCREEN - A STIMULI IS PRESENT IN FOCUS AND PREFERENCE IS UNDERSTOOD.

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COLOR AS STIMULI*

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Training with Individual feedback

HYPOTHESIS 16 The machine will account for difference in colour calibrations in different screens

The feature of understanding the user preference through GSR sensors could be used while setting up the device and calibrating it to each screen it connects to. This is to overcome the inconsistency of screen calibration ; Macs and PCs employ different gamma curves.

CONCLUSION 16 Theory is in place however needs to be tested in order to validate the hypothesis.

The algorithm can learn from the user from both their explicit inputs and physiological datapoints to develop a custom colour profile and get personalise the recommendations.

METHODOLOGY Conceptualisation

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FIG. 8.133 COLOR CALIBRATION DIFFERENCE IN TWO DIFFERENT SCREENS FROM TWO DIFFERENT SELLERS.

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Finding content with Interface design

Problem with current way of decision-making HYPOTHESIS 17 The way the interface is designed increases the chances of user finding content based on how they are feeling

METHODOLOGY Literature review, prototyping

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Users don’t know what is their desired emotional state. We need to inculcate heuristics as a shortcut to make decisions.

So, how will the machine be able to understand what are the users inclined towards watching?

With all the visual clutter and information overload, the user ends up in a state of decisionfatigue.

Endless options to choose from with endless scrolling helps in showcasing the diversity of the content however contributes to the decision fatigue. Choosing a movie is a singleclick interaction, which doesn’t give a sense of constructing a larger decision with multiple smaller decisions.

As the reader can see the vicious circle of cognitive fatigue can be broken by reducing the choice overload and breaking the cycle.

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Why this happens?

Daniel Kahneman, a Nobel Prize winner in Economic sciences refers to two systems in the mind, System 1 & System 2 in his book ‘Thinking Fast and Slow’.21 These systems represent two different modes of thinking in the human brain.

Humans take around 10,000 decisions everyday.

Most of them do not surface as conscious decisions because our brain has repetitively taken those decisions so much so they have become automatic. 95% of decisions we take everyday are automatic, unconscious and super-fast. This is where System 1 is dominant. Refer to the table for a brief difference between both the systems.

System 1

System 2

Around 95% of total decisions

Around 5% of total decisions

Operates automatically and quickly

Conscious and slow

Little / no effort

Effortful mental activity

No sense of voluntary control

Human has agency, choice and concentration

Irrational

Rational

Example of a decision would be to detect an angry face

Example of a decision would be to multiply 43 x 248

FIG 8.134 DIFFERENCES BETWEEN SYSTETM 1 & SYSTEM 2

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System 1 continuously generates suggestions for System 2: impressions, intuitions and feelings. If endorsed by System 2; impressions and intuitions turn into beliefs, and impulses turn into voluntary actions.

System 2 is mobilised when a question arises from which System 1 does not offer an answer, as probably happened when one encounters the multiplication problem 17x24. System 1 has biases, however, systematic errors that it is prone to make in specified ciscumstances.

One further limitation of System 1 is that it cannot be turned off, If a person is shown a word on the screen in a language they know, they will read it-unless their attention is totally focused elsewhere.22 Kahneman describes System 1 as efforlessly originating impressions and feelings that are the main sources of the explicit beliefs and deliberate choices of System 2.

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8.135 2 SYSTEMS IN MIND - ILLUSTRATION BY DAVID PLUNKERT

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Solution Activities that demand operation of System 2 will not make sense here.

The idea is to let users operate automatically on System 1 What do these actions look like?

• Smell this, • Hold this, • Touch this (understand preference by type of touch), • Find this • Watch this (understand via pupil dilation) • Follow this • Play with this object

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Goal : The Solution

Framing & Conceptualisation

1

In the third phase (Play), the goal was to help the user in navigating through the grey space of choosing and not choosing; to design a choosing experience that tricks them into thinking that they’re not choosing.

GUT BASED DECISION : REDUCING ANALYSIS PARALYSIS

Humans become analytical as they grow. They tend to reason more, instead of trusting their guts. The goal is System 1 dominated decision-making. The design opportunity is to design an interaction that helps the user take decisions based on their gut.

2

LESS IS MORE : REDUCING CHOICE OVERLOAD

Too much choice is not always good. This statement is true to an extent where more choices can be demotivating for the user, which leads them to drop the idea of watching altogether. How might we find the design an interface that reduces Choice Overload

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Here, the user dive into a new spectrum of colours.

The focus was on offering abstract options that takes users innately in the direction their mind wants to go. A lot of iterations were tried to address this design opportunity and the goal was always to try navigate through this grey area.

In the final fourth phase (Watch), the user is provided with 3 options that are completely visual and condensed to where their brain has arrived through their natural thought process

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The user interface for the third and fourth phase is divided into three parts (following the first three) viz :

IV

V

VI

PICK ANCHOR COLOUR From a roulette, user taps an anchor colours to narrow preferences

DESIGN A COMPOSITION User creates a colour palette using their gut-feeling

GET RECOMMENDATION User gets movies recommended based on their composition

FIG 8.136 CONCEPTUALISATION TO DERIVE DIGITAL UI ELEMENTS

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(1) Gut-based Decision-making

Interactive playful experiences HYPOTHESIS 18 Users can take decision based on their gut-feeling thorugh an interface

Some developed apps & experiences were taken as inspiration to design the playful experience of designing a composition FLAT UI, PLEASANT NATURELIKE ELEMENTS by Cove

METHODOLOGY

ENGAGING, FLUID ANIMATIONS by Apple Inc.

Literature review, prototyping, usability testing through A-B testing

CLOSE TO LIFE and MESMERISING by GOOGLE

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COVE is an iOS based application that archives feelings in a diary in a creative way. With the use of certain shapes, colours and simulating nature - the playful experience captures the user’s attention. The experience is amped by engaging the auditory senses.

APPLE INC. uses bubbles and collission behaviour while letting users choose their favourite artists to personalise the recommendations. Gravity pulls the bubbles towards the center of the view.

GOOGLE simulated real-life physics with water colour animations to delight the users while they were waiting for a result.

FIG 8.137 COVE FIG 8.138 APPLE MUSIC FIG 8.139 GOOGLE Á SPACE FOR BEING

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The goal is System 1 dominated decision-making. The design opportunity is to help the user take decisions based on their gut

Is there a way to design to help the user avoid the thought of taking a decision at all? A possible solution is to change the perception of choosing and let the user play a game. How could that look like?

For the user to feel like they’re not taking a decision, the context of choosing movies should be removed altogether. That way the user won’t be able to presume a lot of noise like text, name of the movie in a literal way as it is given.

The idea is to give the user a chance to use their imagination by representing a movie in an abstract way.

FIG 8.140 MOVIE OPTIONS LABELLED WITH THE FEELINGS

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Instead of displaying 3 movie posters along with communicating the desired emotional state users want to be in (Fig ) - how could the movies be abstracted further?

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The idea to choose an abstraction form was a function of available datasets online. imDb contains keywords for each movie while a lot of journalistic sources contain movie colour palettes.

Hence, these two were picked up and explored further. Colours give an abstract sense of what would be offered. Hence, Option 2 was picked and the concept will be explored further.

[A] Words make the user think more to guess as to what they would be offered. Also, more words would confuse it.

[A] Word clusters

[B] Colours give an abstract sense (compared to words) of what would be offered. Colours can reduce analysis paralysis FIG 8.141 MOVIE CONTENT ABSTRACTION AS WORD CLUSTERS FIG 8.142 MOVIE CONTENT ABSTRACTION AS COLOUR PALETTES

[B] Colour palettes

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User Experience Marry the content abstraction to the required user-interaction. The desired user-interaction is the one where user’s attention is managed and where they don’t feel the Fear of Missing Out. The stimuli can be presented in a sequence (one after another) and the SCR can be used as an indicator of specific emotional arousal. The goal is no to overwhelm the users with multiple choices.

CONTENT ABSTRACTION

SEQUENTIAL STIMULI

X

=

?

FIG 8.143 CONCEPT FOR USER EXPERIENCE

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Inspiration board

Fig 1

Fig 2

Fig 3

Fig 4

Fig 7 Fig 5 Fig 1 Roulette game rotation Fig 2 Colour fusion Fig 3 Movie Fig 4 Spin some wheel

Fig 6 Fig 5 Spin again - Reelgood’s Netlfix Roulette gamifies the movie-choosing process Fig 6 Google Movies expands a movie you select to draw the attention towards the icon Fig 7 Slot Machine’s jackpot reward is a delightful happy experience

FIG 8.144 INSPIRATION BOARD FOR UX

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Storyboard ( Anchor Colour ) PART IV PICK ANCHOR COLOUR Emotions flow into each other. Similarly, the continuity has been extended to the way colour palette appears on the screen. Colours follows another colour and a spectrum of colours is created.

1

2

3

1

2

L 1

Roulette starts

1

L

2

1

L

3

2

1

FIG 8.145 STORYBOARD FOR PHASE 3 & 4 UX

Round 1 complete

Retracts

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Stops

Expands

Flow

Flows

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PART V DESIGN A COMPOSITION Repeat the same process to compose a colour palette. The result is a unique compositional artwork.

Repeats for 4th time

Repeats

PART VI GET RECOMMENDATION The same artwork is packaged as a cover for a Disk. The disk is animated to be inserted in the drive and 3 movie recommendations appear.

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packaged as an art cover that can be saved

3 movies recommended at the end

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User Interface PART IV PICK ANCHOR COLOUR Version 1 Shape wise - it is discrete, colors dont flow into each other, much opposite of how emotions are Version 2 Seems like a spectrum, forming a continous range, like emotions

FIG 8.146 VERSION 1

FIG 8.147 VERSION 2

The rounded forms in V2 also help in communicating that it is a friendly and leisure app while V1 looks corporate, almost like part of some technical presentation.

FIG 8.148 FINAL UI (PART IV)

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After choosing the colour, the command to confirm the colour choice is to tap on thte scroll wheel. The colour flows down. Frames have been shared.

In continuation of the meditative experience, the experience is designed so the user stays in the state of flow and their decisions are not cut by inhibitions.

FIG 8.149 TAP & FLOW UI

A mockup is shown with real colours and how it is integrated with the physical interface by tapping on the scroll wheel.

PART V DESIGN A COMPOSITION

FIG 8.150 PHYGITAL UX WITH TAP & FLOW

Option 1 Colour interaction was a limitation. With mixing of colours, the colours create their own pocketst and distort. Attention is grabbed by the form rather than colour, while the latter was the primary intention.

Option 2 Flat, clean, simplified minimal UI - gets rid of all visual disturbance/chaos and works with very basic & foundational visual elements. Worked with pure hues equally distributed in the screen.

To bring user’s attention to the colour, blurr & sharpness were used.

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FIG 8.151 OPTION 1 COLOUR INTERACTION

FIG 8.152 BLURR (SIDES) = FAR AWAY, FOCUS (CENTRE) = CLEAR

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Color psychology rule engine

A Colour psychology rule engine (CPRE) is proposed to help users find the colours they would be inclined towards watching. The CPRE will be able to connect colours to the identified desired affective state and prioritise them for easier choosing.

Experiment In this particular scenario, when the viewer is already tired, let’s see what the CPRE will prioritise.

This rule engine will be trained with a large pool of datasets while it gets better and personalised with the feedback from the user.

FIG 8.153 ‘CPRE’ FLOW DIAGRAM

Color theory says when a user is tired, the user usually wants something rejuvenating and refreshing. Hence, the CPRE will prioritise a stream of pastels, like Aqua which is rejuvenating and refreshing due to absence of red.

The user’s heart will suddenly tell them to choose it since they are most likely to be inclined towards it. There is no involvement of System 2 thinking here.

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CONCLUSION 18 Theory is in place however usabilitytesting will validate the hypothesis.

Studies suggest that users are able to take gut-feeling induced decisions when the System 1 of their brain is activated. Interface was designed in a way to induce feeling.

FIG 8.154 LIMITED OPTIONS. THE EXTENDED ROULETTES END AFTER A CERTAIN LENGTH DEPICTING LIMITED SCROLLING (AS OPPOSED TO ENDLESS SCROLLING IN CURRENT UIS)

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(2) Reducing choice overload

“One effect (of choice), paradoxically, is that it produces paralysis, rather than liberation.

With so many options to choose from, people find it very difficult to choose at all.”

- Barry Schwartz The term “choice overload” was coined by Alvin Toffler in 1970. It occurs when people are in a situation where many equivalent choices are available to them. 23 Hick’s Law predicts that the time and the effort it takes to make a decision, increases with the number of options.24

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FIG. 8.155 HICK’S LAW DIAGRAM. MORE OPTIONS ~ MORE TIME SPENT IN TAKING THE DECISION

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To break the vicious circle of cognitive fatigue, choice overload should be avoided Research found that the satisfaction of choices by number of options available follows an inverted U model—in other words, there is a sweet spot with not too few, and not too many options—just enough to maximise our perception of freedom and our mental well-being.

If the user has too few options, they feel frustrated. Too many, and we may experience analysis paralysis, fear of a better option, and even regrets afterwards. Did they really make the right choice? Wasn’t another option better than the one they picked?25

FIG. 8.156 DIMINISHING SATISFACTION WITH INCREASE IN OPTIONS

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[A]

[B]

Having too many options with equally perceived hierarchy can cause analysis paralysis

Fewer and clearer options frequently are rated by users as having a better user experience.

Humans are strange.

They say they want a variety of options; more the merrier. However, when they get them, it only confuses them and hinders decision-making

In contrast, systems with fewer and clearer options frequently are rated by users as having a better user experience.

FIG. 8.157 COMPARISON OF TWO REMOTE CONTROLS ÚSER-EXPERIENCES

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Bandersnatch (interactive movie) on Netflix uses an amazing technique in their interactive film for the user to take a decision in a limited time frame with only two options.

Headspace interactive special ‘Unwind your mind’ personalizes the experience according to the viewer’s mood or mindset. Within the experience, viewer is given a choice of 3 to choose.

FIG. 8.158 INTERACTIVE DOCUMENTARY UX - UNWIND YOUR MIND BY HEADSPACE ON NETFLIX WITH ONLY 3 OPTIONS FIG. 8.159 INTERACTIVE MOVIE BANDERSNATCH ( BLACK MIRROR ) UX ON NETFLIX WITH ONLY 2 OPTIONS

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CURRENT

FIG 8.160, 161, 162 : UNCOUNTABLE TILES : NETFLIX HOMESCREEN ZOOMED OUT TO FULL WEBPAGE

PRO + Diversified content + On-demand content CON - Choice Overload : Shoving a lot of content in user’s eyeballs upfront - Analysis Paralysis : Inability to take decision and overthink - Even after having uncountable choices, users are unable to look everything available on web due to unavailability of universal search feature.

FIG 8.161

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FIG 8.162

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PROPOSED

(1) PICK ANCHOR COLOUR

(2) CHOOSE COLOURS WHICH YOUR HEART SAY

PRO + Step-wise decision by playing a game + Clear whitespace + Reduction in clutter + No infinite scrolling

(3) GET RECOMMENDATIONS FIG 8.163 RECOMMENDED USER-EXPERIENCE

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IV Pick Anchor Colour

FIG 8.164

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V Design a Composition CONCLUSION 17 Theory is in place however usabilitytesting will validate the hypothesis.

Interface is designed in a way that (1) Induces Gut-based decisionmaking (2) Reducing choice overload by limiting the no. of options FIG 8.165

VI Get Recommendation

FIG 8.166

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FIG 8.164 PICK ANCHOR COLOUR FIG 6.165 DESIGN A COMPOSITION FIG 8.166 GET RECOMMENDATIONS

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Data-driven approach to match movies and colours Colours can affect us psychologically and emotionally without us even becoming aware.

HYPOTHESIS 19 An algorithm will be able to label movie-options according to their feel

In filmmaking, color is one of the most powerful means of conveying information to the audience.

Filmmakers and cinematographers use certain colours and aesthetics to reinforce the idea that they are trying to convey to the spectators, to bring attention to a key visual theme, to show a character’s journey, and more.

METHODOLOGY Literature Review, Conceptualisation

FIG 8.167

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Colours and Filmmaking

FIG 8.168 VARIOUS EVIDENCES ESTABLISHING FILMMAKERS PLAY WITH COLOURS AS AN INTEGRAL PART OF THEIR VISUAL ESSAYS

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Algorithm

The algorithm helps derive a colour palette from each movie present in the dataset.

Using a data-driven approach - it elicits dominant colours from consecutive frames and juxtapose them to an extent so as to not lose meaningful colour information and match them to the compositions made by users. This helps the user skip the visual clutter bringing them closer to the movie’s feel.

FIG 8.169

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FIG 8.170 JUXTAPOSITION OF FRAMELINES OF MIYAZAKI’S PRINCESS MONONOKE

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Using computer vision, it will find similar movie colour palettes to your composed palette (made with your gut-feeling) and recommend movies by their feel.

CONCLUSION 19 The algorithm model needs to be tested in order to validate the hypothesis.

FIG 8.171 SOUL & FINDING NEMO COLOUR PALETTE

[Final composition] FIG 8.172 PALM SPRINGS COLOUR PALETTE IS JUST ONE COLOUR CHANGE AWAY FROM SOULD & FINDING NEMO

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FIG 8.173 REMOVE CYAN AND ADD PURPLE TOT SOUL, A DARK PALETTE APPEARS AND THE RECOMMENDATION CHANGES TO FROZEN

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CURRENT

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PROPOSED

FIG 8.174 CURRENT VS PROPOSED UI

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Sensing Touch

HYPOTHESIS 20 Click wheel can detect actions like tap, scroll and press

iPod Classic scroll wheel

Low-fidelity prototype

The primary technology that the click-wheel demonstrates is that of capacitive sensing. When two metal plates are placed very close to one another, without coming into contact, a current passes through the plates, energy is stored, but once the current is taken away, the stored energy creates a current on its own. This is how a capacitor gathers and stores energy.26

A low fidelity sensor was developed with a metal strip, by following the instructions in the video27 to make a DIY capacitive sensor switch. The metal foil can also be folded so increases the possibility of being placed within a curvy surface as well.

METHODOLOGY Benchmarking, Prototyping and testing

FIG 8.175 CLASSIC IPOD WITH CLICK WHEEL

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Detection of touch by the capacitive sensor. The LED is not glowing here.

CONCLUSION 20 Hypothesis is true. The click wheel can detect actions like tap, scroll and press.

FIG 8.176

Detection of touch by the capacitive sensor.

INPUT = touch, OUTPUT = LED glow

FIG 8.177

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Summary of Conclusions Hypothesis

Conclusion

Hypothesis

Conclusion

1

The pillow feels inviting enough for the user to be motivated to touch it

The pillow’s unique combination of color, material and finish invites different users to touch and explore the pillow. However, iterations are expected since it’s yet to be validated with a large group of users.

6

User’s current emotional state can be elicited using physiological data

There’s been reliable co-orelation between user’s current affective state and physiological signals combined. Theory of an emotion-recognition algorithm is in place, however needs to be validated further.

2

The pillow feels comfortable and huggable during interaction

Hypothesis is true. The form of the pillow comfortable and huggable. However, iterations are expected since it is yet to be validated with a large group of users.

7

These data-points are reliable indicators of the current affective state of the user (a) individually (b) in combination

Hypothesis is partially true. The experiment concludes the potential however needs to be validated further.

3

The presence of a hand can be detected in the pocket

A successful low-fidelity prototype of a functional capacitive sensor depicts that the hypothesis is true.

8

The pillow can adjust its breathing according to the user

Such technology exists that can detect user’s breathing and adjust its breathing according to the user and gradually slow down the breathing rhythm to a calming rate.

4

User’s current affective state is a factor in determining what they want to watch

Hypothesis is true.

9

Users will synchronize their breathing to the pillow’s rhythm

The hypothesis is true. A study shows a positive coorelation between user’s breathing rhythm and an object

10

Breathing helps in rejuvenating cognitive resources and enhancing decision-making capability

The hypothesis stands true. Studies show the effects of breathing patterns on heart rate variability and decision-making in business cases.

5

It is possible to record and extract Hypothesis stands true. Consumer electronic physiological data in real time products have made it possible to record and while sitting extract physiological data in real time.

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Hypothesis

Conclusion

11

Visual experience will (i) position the user in sub-conscious calming level and (ii) keep them engaged

Fluid animation and calming uncomplicated visuals are used. The design intervention needs to be tested in order to validate the hypothesis further.

12

The set of algorithms will be able to identify user’s desired affective state & personalise recommendations

Theory is in place however the set of algorithms need to be tested in order to validate the hypothesis.

13

The feedback loop will help in personalising recommendations

Theory is in place however the set of algorithms need to be tested in order to validate the hypothesis.

Colours represent how we feel and be used as heuristics for content-choices

Each colour has the ability to evoke different emotions and various colors represent various moods.

14

Gap : The difference in preferences and perception are different for individuals and depend on a variety of factors. explored further in Hypothesis 16 15

Each individual’s difference in preferences and perception of colour can be accounted for by the algorithm

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Hypothesis

Conclusion

16

The machine will account for difference in colour calibrations in different screens

Theory is in place however needs to be tested in order to validate the hypothesis.

17

The way the interface is designed increases the chances of user finding content based on how they are feeling

Theory is in place however usability-testing will validate the hypothesis. Interface is designed in a way that (1) Induces Gut-based decisionmaking (2) Reducing choice overload by limiting the no. of options

18

Users can take decision based on their gut-feeling thorugh an interface

Theory is in place however usability-testing will validate the hypothesis. Studies suggest that users are able to take gut-feeling induced decisions when the System 1 of their brain is activated. Interface was designed in a way to induce feeling.

19

An algorithm will be able to label movie-options according to their feel

The algorithm model needs to be tested in order to validate the hypothesis.

20

Click wheel can detect actions like tap, scroll and press

Hypothesis is true. The click wheel can detect actions like tap, scroll and press.

21

OTT platforms can be connected to the recommendation platform for a seamless experience

Almost all of the content-streaming platforms like Netflix, Prime videos, Hotstar, Hulu.. have developed APIs (Application Programming Interface) which can be integrated with different platforms / apps / systems as action buttons for the user

Theory is in place however needs to be tested in order to validate the hypothesis.

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Closure The solution at core requires all movies to be converted into colour palettes. A limitation to this approach is that not all movies are built being mindful of colour perception. Mostly animated movies and certain directors are famous for intentionally using colours to accentuate a mood and drive a point. Narrowing down movies based on isolated colour preferences (without content) might / might not help the user achieve their desired affective state. Having said that, this is a hypothesis to be tested further. Usability testing along with expert feedback would be critical to conclude the hypothesis.

Another hypothesis to validate product’s market fit. Though this project builds on the pain-points of real consumers, it was developed to solve a very narrow use-case which might not be enough to solve the larger problem of movie recommendation in any context. It depicts the problem solving ability of the designer.

It was assumed that a physical experience however effortful, would be a desirable solution

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to the user as opposed to the fatigued digital experience. However, there’s a caveat to this.

Users prefer low effort and high return on effort.

According to BJ Fogg’s behaviour model, users are motivated to change behaviours when effort involved is low. Will the user be able to put in this amount of effort as a trade off to get a movie recommendation?

An expert suggested that business-wise successful products are ones which are ‘familiar but new’. The question is - how will we motivate the user to adopt this completely new way to choose movies? Without thinking much, we will see how this works. Again, this could be tested by advertising and getting people to sign up for using the product.

This project is a WIP and is just an initiation into the world of Artificial Intelligence & Human Intelligence to solve the decision-conundrum problem for OTT platforms. On a larger note, this project is Step 0 into the world of Designing for Better Decisions by leveraging an interdisciplinary

(in an ideal modern world - transdisciplinary) approach to problem solving. It presents a massive possibility for better decisions to be realised in each sphere of human life.

Better decisions is the common denominator for different professionals speak - from product managers, to entrepreneurs, to researchers to C-Suite members.

What started as an attempt to develop a conversation piece to convey the power at the intersection of AI - Engineering - Design converted into a humanising tech solution. Humanising tech field is emerging as seen across various industries .They are continuously realising the importance of placing users at the center and they are trying to work towards either radically or an iterative fashion.

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SYNTHESIZE DESIGN DELIVER

EPILOGUE

Reflections Glossary References Contributors Colophon Contact Information

Reflections It was a bold move to expand my learnings into an unknown new domain and choose this opportunity as a capstone project. “Experience Design in an enterprise context by the application of materials and expansion of interdisciplinary interests” By the virtue of being a curious person and the available stimuli around me, it’s natural for me to pick up on new fields which might seems daunting otherwise. The unsaid network mapping of how do these fields, people, practices integrate with my practice is an automatic activity in my brain now.

My Fractal internship proved to be a valuable milestone in this curious journey. At first, it helped me understand the validity of my practice in solving valid real-life problems and gaining in-depth insights into the field of experience, interaction and new media design which otherwise I’d have access at a master’s level stage.

Everyday felt like it would become another incomplete project that would bite dust on the shelf. Due to COVID, the focus of the project shifted

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from Event-centric to Remote-centric and this fear intensified. However, a 6 month project was continued because I wanted to build something that can be user-tested.

With this design process in mind and informed understanding of tech, I would want to carry this journey forward and make an impact that I wish to and I am trained for.

The nerve-wrecking presentations, honest client feedbacks, reflecting on the process and getting better with each iteration - this whole process was running live throughout. The second set of User-research and synthesis process took much lesser time and resources to happen, even though they were set in a complex scenario of not being in person. I learnt how to apply design research methods properly and I’m confident to continue doing so.

Something to takeaway and improve upon is understanding the scope of the project and judging the right timeline for the project..

I met inspiring people. Each discussion actively shaped my abilities. I grew to be more articulate (can work on that a lot more). Projects with global employers open you up to an entirely new paradigm of contextual case-studies, reality and self-management possibilities. Ironically, by dedicating time to someone else you learn a lot more about yourself if reflected right.

The tech world is limited only by imagination.

Technology like Facial recognition, Sentiment Analysis and most others have become accurate, efficient and faster with iterations over the years.

That’s what engineers and developers are known for - to iterate and bring cutting-edge technology to the table over the years. A designer’s role is to imagine that desirable experience, keep the user’s

Another space I could be more mindful is being gender-neutral while sketching, addressing the user as ‘they’ and many more instances. This directly doesn’t relate to the project however as a designer, we influence the lives of our users and we should do it responsibly.

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desirability at the centre. Instead of thinking, “Does the tech exist to deliver this experience?” the right question to ask is, “Is the experience compelling enough to make a developer excited to develop the technology even if 50% of it seems feasible today?”

This was and will remain the prime focus informed by the current trend in technologies. The user’s desired experience should be at the centre of my process.

“Best ideas today don’t emerge in disciplines, they occur at the intersection of disciplines.

Let’s break down the silos” - Rory Sutherland, Vice Chairman, Ogilvy UK

A significant learning from the industry experience was gaining this insight about my practice and understanding what I bring to the table.

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Glossary Cognition : The mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.

Experience : Experience is the process through which conscious organisms perceive the world around them. Experiences shape memory and memory shapes perception.

Perception : The organization, identification, and interpretation of sensory information in order to represent and understand the presented information or environment.

Affect: Affect is the collective term for describing feeling states like emotions and moods.

Affective states may vary in several ways, including their duration, intensity, specificity, pleasantness, and level of arousal, and they have

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an important role to play in regulating cognition, behavior, and social interactions.

Feeling: In psychology, feeling is usually reserved for the conscious subjective experience of emotion.

Emotion: A conscious mental reaction (such as anger or fear) subjectively experienced as strong feeling usually directed toward a specific object and typically accompanied by physiological and behavioral changes in the body Mood In psychology, a mood is an affective state. In contrast to emotions or feelings, moods are less specific, less intense and less likely to be provoked or instantiated by a particular stimulus or event.

Moods are typically described as having either a positive or negative valence. In other words, people usually talk about being in a good mood or a bad mood.

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Kahneman, Daniel. Thinking, Fast and Slow. Farrar, Straus

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Accessed 7 Feb, 2021

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TOOLS

VISUAL DESIGN

Dell Inspiron 7560

Medium.com

MacBook Pro

googlefonts.com

iPad Pro

pangram pangram type foundry

Adobe Indesign

Lines of Inquiry - NID Graduation Show 2020

Adobe Illustrator Adobe Photoshop Adobe Premiere Pro Microsoft Word Microsoft Excel Figma Autodesk AutoCad Blender Processing Arduino Google Drive google.com wikipedia.com youtube.com

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Image sources

Section 7/Design Fig 7.00 : Manan Pahwa Fig 7.01 - 7.16 : Manan Pahwa Fig 7.17 : Various, compiled by Manan Pahwa Fig 7.18 : https://www.parshaops.gq/ProductDetail. aspx?iid=196005612&pr=56.99 Fig 7.19, 7.20 : Manan Pahwa Fig 7.21 : https://possible.in/deep-breathing-a-simple-exerciseto-rejuvenate-your-mind-and-health.html Fig 7.22 : https://www.health.harvard.edu/mind-and-mood/

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Fig 8.00 - 8.08 : Manan Pahwa Fig 8.09 - 8.12 : Manan Pahwa & Sangam H Fig 8.13 - 8.16 : Manan Pahwa

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Section 8b/Evidences Fig 8.17 : Various, compiled by Manan Pahwa

https://imotions.com/hardware/tobii-pro-glasses-3/,

Fig 8.82 : Manan Pahwa & Amogh Jadhav

Fig 8.18 - 8.29 : Manan Pahwa

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Fig 8.83, 8.84 : https://somnox.com/wp-content/

Fig 8.30, 8.32, 8.33, 8.35, 8.36, 8.38 : Adapted from the book

https://www.emotiv.com/epoc-x/, https://imotions.

- Indian anthropometric dimensions for Ergonomic design practice’’ by Debkumar Chakrabarti.

Fig 8.31, 8.34, 8.37 : Manan Pahwa

com/hardware/shimmer3-ecg/

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Fig 8.71 : https://support.apple.com/en-us/HT204666

Fig 8.85 - 8.89 : Manan Pahwa

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Fig 8.90 : Manan Pahwa & Amogh Jadhav

Fig 8.39 - 8.42 : Manan Pahwa

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Fig 8.91 : Manan Pahwa

Fig 8.43, 8.44 : Manan Pahwa & Sangam H

Recognition_Review_of_Sensors_and_Methods/

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Fig 8.59 : Pantone

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Fig 8.94 : https://www.youtube.com/watch?v=Ztax9lCE-Mk

Fig 8.60 - 8.62 : Manan Pahwa

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Fig 8.95 : Manan Pahwa

Fig 8.63 : Manan Pahwa & Sangam H

publication/41393262_The_Coherent_Heart_Heart-

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Fig 8.64, 8.65 : Manan Pahwa

Brain_Interactions_Psychophysiological_

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publication/326841515_Identifying_the_Causes_ of_Drivers%27_Hazardous_States_Using_Driver_ Characteristics_Vehicle_Kinematics_and_ Physiological_Measurements/figures?lo=1

Fig 8.75 : https://www.sciencedirect.com/science/article/abs/ pii/S0166361516303104?via%3Dihub Fig 8.76 : https://imotions.com/blog/gsr/

Fig 8.68, 8.69 : Twitter

Fig 8.77 : Manan Pahwa

Fig 8.70 : https://imotions.com/hardware/gazepoint-gp3-hd/,

Fig 8.78 - 8.81 : Amogh Jadhav

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photos-that-make-everyone-love-it-cool-live-photos/ cool-retro-live-wallpaper-for-your-iphone-xs-fromeverpix-live-cool-live-photos/ Fig 8.104 : https://9to5google.com/2019/06/20/google-artband-video/

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Section 8b/Evidences Fig 8.105 : https://www.nasa.gov/mission_pages/chandra/ multimedia/Chandra_Images_archive_1.html

watch?v=dHy9VrtbHIE

Fig 8.146 - 150 : Manan Pahwa & Amogh Jadhav

Fig 8.127 : Tim Holmes / Manan Pahwa

Fig 8.151 - 152 : Manan Pahwa & Aiman Verma

Fig 8.106 : Manan Pahwa

Fig 8.128 : Google search - Sentiment analysis using emojis

Fig 8.153 - 154 : Manan Pahwa & Aiman Verma

Fig 8.107 : Manan Pahwa & Aiman Verma

Fig 8.129 : https://imotions.com/blog/z-tree/

Fig 8.155 : https://uxplanet.org/design-principles-hicks-law-

Fig 8.108 : Manan Pahwa & Amogh Jadhav

Fig 8.130 - 131 : https://imotions.com/blog/gsr/

Fig 8.109 : https://weheartit.com/entry/293172225

Fig 8.132 : Manan Pahwa

Fig 8.156 : https://nesslabs.com/overchoice

Fig 8.110 : Manan Pahwa & Prajjwal Chandra

Fig 8.133 : https://eizo.com.cy/library/basics/lcd_display_

Fig 8.157 : https://www.walmart.com/ip/DIRECTV-Remote-

Fig 8.111 : Manan Pahwa & Amogh Jadhav

gamma/index.html

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Fig 8.135 : “Illustration by David Plunkert https://www.nytimes.

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com/2011/11/27/books/review/thinking-fast-andslow-by-daniel-kahneman-book-review.html”

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MANAN PAHWA • NATIONAL INSTITUTE OF DESIGN

shop/product/MJFN3ZM/A/apple-tv-remote

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Fig 8.144 : Various

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Fig 8.145 : Manan Pahwa

Fig 8.174 : Apple - Classic iPod

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Fig 8.175 - 176 : Manan Pahwa & Amogh Jadhav

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Contributors People who endowed their love and support for this project and document to come alive Aashi Bhaiji Abhishek Gaur Abuzar Aiman Verma Amardeep Behl Amogh Jadhav Anand Nirmal Aniket Koyande Anil Patel Anushka Ashok Ashul Aggarwal Ayushi Bansal Ayushi Srivastava Bharati Bhumica Kumar Bourbon, Amy and Feni Dad Deepanshu Raghuvanshi Devansh Khajanchi Dhairya Wadhwa Divya Chadha Divya Parekh Eemon Roy

Garima Beniwal Gopal Singla Harshali Paralikar Ishita Verma Jayneel Shah Kamlesh Bhai Kapil Grover Kautuk Trivedi Kedar Mogarkar Kushagra Singh Lajpat Rai Chaudhary Lalitha Poluru Larry Camilo Lovneet Bhatt Madonna Thomas Manik Narula Mom Mudita Pasari Nainisha Dedhia Nakul Makharia Neel Koradia Niketa Pahwa Nirmal Dhillon

MANAN PAHWA • NATIONAL INSTITUTE OF DESIGN

Param Venkataraman Paramazeez K Paritosh Chaudhary Parth Ahuja Parth Dhonde Partha Mahanta Pawan Bansal Piyush Bhai Prabhuta Verma Prajjwal Chandra Prathamesh Patel Priya Sathiyam Rahul Agarwal Raj Pahwa Rajesh Verma Rayika Biswas Sachin Sachar Sahil Thappa Sangam H Sarath Chandra Sarath Nair Saurabh Kabra Saurabh Singh

Sharmila Shah Shefali Bohra Shivani Gupta Shreya Chakravarty Siddharth Kataria Sneha Srinivasan Somya Uppal Sunaina Desai Sweety Taur (guide) Tejal Sharan Tia Kansara Tim Holmes Uday Chaudhary Urja Jhaveri Usha Bansal Vikas Bansal Vikash Challa Vikram Kalidindi Vineet Nandkishore Vishakha Pahwa Vivek Sheth Yash Makwana

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Thank you everyone!

Dear reader, This is a WIP project and the beginning of the professional career. The aim is to see this or iteration of the solution in user’s hands andhelp them with better content decisions.

If you haven’t read Volume 1, I insist you to read it.

VOLUME 1 is a process map to achieve the final use-case to build a tangible solution on in VOLUME 2. It focuses on the navigation from multiple diverging and converging diamonds Opportunities to Strategy, Discover to Define, Empathise to Synthesize.

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Colophon This document has been designed and written digitally by Manan Pahwa.

This document has been set in: Chivo (Open) is an Omnibus-Type grotesque Sans Serif typeface family. Chivo elegance makes it ideal for combining with the strength of Chivo Black for continuous reading.

Neue Machina (Free) is a powerful and meticulously crafted typeface inspired by the aesthetics of robotics and machines — a font suited for the future of technology. Designed by Mat Desjardins, released as a product of Pangram Pangram Foundry.

FRUTIGER is designed by Adrian Frutiger and released by Lino Type Foundry.

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Contact Information Fractal Analytics

Manan Pahwa

Goregaon (E)

Karnal 132001

Mumbai 400063

Haryana, India

Maharashtra, India mananpahwaa@gmail.com fractal.ai

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www.mananpahwa.com

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