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
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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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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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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
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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
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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.
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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
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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
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1
2
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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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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
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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.
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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.
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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
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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
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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.
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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.
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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.
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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.
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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.
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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.
DESIGN FOR BETTER DECISIONS
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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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/
Section 8a/Deliver Fig 7.25 : https://www.youtube.com/ watch?v=kuoBEJ9DkJM&feature=emb_title Fig 7.26 : https://www.fastcompany.com/90169644/calm-
Fig 8.00 - 8.08 : Manan Pahwa Fig 8.09 - 8.12 : Manan Pahwa & Sangam H Fig 8.13 - 8.16 : Manan Pahwa
interfaces-are-here-and-theyre-wonderful Fig 7.27 : Manan Pahwa Fig 7.28 : https://www.nytimes.com/2020/04/03/smarterliving/talking-out-problems.html Fig 7.29 : https://www.ibm.com/blogs/cloudarchive/2017/01/tjbot-chatbot-watson/
relaxation-techniques-breath-control-helps-quellerrant-stress-response Fig 7.23 : Rollin McCraty, Emotional Stress, Positive Emotions and Psychophysiological Coherence, Researchgate https://www.researchgate.net/ publication/295921152_Emotional_Stress_Positive_ Emotions_and_Psychophysiological_Coherence Fig 7.24 : Juan Pablo Kalawski, Effects of tenderness on problem solving [electronic resource], ResearchGate https://www.researchgate.net/ publication/34742923_Effects_of_tenderness_on_ problem_solving_electronic_resource/citations
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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
https://imotions.com/hardware/shimmer3-gsr/,
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/
uploads/2019/06/20191203_Somnox_Whitepaper. pdf
Fig 8.71 : https://support.apple.com/en-us/HT204666
Fig 8.85 - 8.89 : Manan Pahwa
Fig 8.72 : https://www.researchgate.net/
Fig 8.90 : Manan Pahwa & Amogh Jadhav
Fig 8.39 - 8.42 : Manan Pahwa
publication/338723696_Human_Emotion_
Fig 8.91 : Manan Pahwa
Fig 8.43, 8.44 : Manan Pahwa & Sangam H
Recognition_Review_of_Sensors_and_Methods/
Fig 8.92 : https://3boxlabs.com/
Fig 8.45 - 8.58 : Manan Pahwa
figures?lo=1
Fig 8.93 : https://weheartit.com/entry/293172225
Fig 8.59 : Pantone
Fig 8.73 : https://www.mdpi.com/1424-8220/18/7/2074
Fig 8.94 : https://www.youtube.com/watch?v=Ztax9lCE-Mk
Fig 8.60 - 8.62 : Manan Pahwa
Fig 8.74 : https://www.researchgate.net/
Fig 8.95 : Manan Pahwa
Fig 8.63 : Manan Pahwa & Sangam H
publication/41393262_The_Coherent_Heart_Heart-
Fig 8.96 - 8.99 : Manan Pahwa & Amogh Jadhav
Fig 8.64, 8.65 : Manan Pahwa
Brain_Interactions_Psychophysiological_
Fig 8.100 : https://dribbble.com/shots/2885291-Expansion-gif
Fig 8.66 : https://imotions.com/blog/z-tree/
Coherence_and_the_Emergence_of_System-
Fig 8.101 : https://shotstash.com/photo/water-ripples/
Fig 8.67 : https://www.researchgate.net/
Wide_Order/figures?lo=1&utm_
Fig 8.102 : Manan Pahwa & Prajjwal Chandra
source=google&utm_medium=organic
Fig 8.103 : https://anupghosal.com/12-features-of-cool-live-
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
DESIGN FOR BETTER DECISIONS
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
Fig 8.112 - 113 : IMDb
Fig 8.134 : Based on book ‘Thinking Fast and Slow’
Fig 8.114 - 115 ; www.vox.com/2018/11/21/18106394/why-
Fig 8.135 : “Illustration by David Plunkert https://www.nytimes.
your-netflix-thumbnail-coverart-changes Fig 8.116 : Manan Pahwa Fig 8.117 - 118 : Built by Manan Pahwa on Jame’s Russel Circumplex Model of Affect 2D valence-arousal map Fig 8.119 : Manan Pahwa Fig 8.120 : https://www.usertesting.com/blog/color-uxconversion-rates
com/2011/11/27/books/review/thinking-fast-andslow-by-daniel-kahneman-book-review.html”
Fig 8.158 : https://www.headspace.com/netflix Fig 8.159 : https://www.thewrap.com/black-mirrorbandersnatch-data-annabel-jones-charlie-brooker/
Fig 8.137 : cove-app.com
Fig 8.163 - 166 : Manan Pahwa
Fig 8.138 : https://ux.stackexchange.com/questions/113987/
Fig 8.167 : https://towardsdatascience.com/exploring-
creating-a-bubble-ui-layout-like-used-in-the-applemusic-app Fig 8.139 : https://vimeo.com/411540083 Fig 8.140 - 143 : Manan Pahwa
MANAN PAHWA • NATIONAL INSTITUTE OF DESIGN
shop/product/MJFN3ZM/A/apple-tv-remote
Fig 8.160 - 162 : Netflix
Fig 8.122 - 124 : https://www.youtube.com/ watch?v=dtLBMBs_S9E&t=265s
RC66RX/102257613, https://www.apple.com/in/
Fig 8.136 : Manan Pahwa
Fig 8.121 : Tonoy Sharma
Fig 8.125 - 126 : https://www.youtube.com/
quick-decision-making-3dcc1b1a0632
chromatic-storytelling-with-r-part-1-8e9ddf8d4187 Fig 8.168 : Various, Medium & Youtube Fig 8.169 - 170 : https://towardsdatascience.com/exploringchromatic-storytelling-with-r-part-1-8e9ddf8d4187
Fig 8.144 : Various
Fig 8.171 - 173 : Manan Pahwa
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
DESIGN FOR BETTER DECISIONS
www.mananpahwa.com
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