Final report - embodying intelligent behaviour in social context

DBM211 - Embodying Intelligent Behaviour in Social context

DBM211 - Embodying Intelligent Behaviour in Social context

Group Report Semester B - Quartile 3 2016

Teaching Professor: Dr. Ir. Emilia Barakova

Submitted by Marijn Bults Eleni Economidou Thijs Roeleven Ivar Kraaijevanger Bastiaan van Hout

A report submitted in fulfillment of the requirements for the course DBM211 Department of Industrial Design TU/e

Table of Contents Design Brief 07 Vision 11

2.1 Overall vision on social agents

12

2.2 Product oriented vision

13

Design Ideation 15

3.1 Cognition 16

3.2 Concept 18

3.3 Design rationale 22

Prototype 25

4.1 Physical prototype 26

4.2 Electronics 28

4.3 Data Analysis 30

4.4 Application 36

Scenarios 41 Discussion 43 Evaluation 49 Conclusion 51 References 53

4

Appendices 57 Reflections 73

5

1

Design Brief

Introduction The

notion

intelligent social

of

design

behaviour

context

for

in

requires

a

some

prediction of high or low levels of

blood

prevent

sugar

can

moments

of

help

to

discomfort

defining before the entire aim

by acting upon them before they

of the assignment is understood.

actually

Initially,

context

group decided to address this

needs to be defined. Within the

problem within the social agent

frame of this elective, our group

framework.

the

social

occur.

Therefore,

the

chose to approach the intelligent agents framework in the social

With the social context defined,

context of young children and

an agent was designed, including

their

its

caregivers;

specifically,

children with diabetes.

intelligent

agent’s

behaviour.

presence

is

The

deemed

necessary, since the intelligent Diabetes Mellitus is a group of

behaviour needs to be embodied

diseases that requires regular

to have the users interact with

monitoring. Either the pancreas

something

does not produce enough insulin

within

or the cells of the body do not

young children).

our

tangible. target

Especially group

(i.e.

respond properly to the produced insulin and

[1].

treatment

injections

8

The

prevention

involves

insulin

timed

sugar

or

After careful research and design iterations,

the

down

options

the

team

narrowed to

smart

intakes. If this type of treatment

wearable products in the form of

does

the

a bracelet called ‘Mbrace’. Its

consequences could lead to hypo-

shape allows the child to wear it

, or hyperglycemia; both being

at all times. It monitors some of

phenomena that can have serious

the main bodily functions, such

consequences for the functioning

as body temperature, heart rate

of the body. Furthermore, the

and

not

occur

on

time,

perspiration,

continuously

whilst are

blood

monitored

sugar

levels

(BSL)

incidentally.

test and they can act accordingly.

More

information about this can be found

To assist and support their children,

under the ‘Embodiment’ chapter.

parents receive notifications on their smartphone via a dedicated

By monitoring the body continuously,

application. If someone else is in

the bracelet can potentially predict

charge of taking care of the child,

what kind of state the child is

the bracelet is communicating with

in using the learning algorithm in

the, at that time, caregiver in

Matlab. Consequently, it calculates

charge

the body’s state according to its

of hardware that is connected to

input variables. If a change of

the

state is detected, the agent gives

connectedness between the patient

continuous feedback accordingly by

and

changing the colour of an RGB-LED

child forgets to undertake action

and a vibration motor, respectively

to either high or low blood sugar

on and in the bracelet. This way,

levels

the user can check the blood sugar

immediately

levels (in approximation). If the

occasion, he or she can help the

blood sugar levels are approaching

child

dangerous

levels and taking further required

zones,

the

user

is

notified to perform a blood sugar

via

an

bracelet, the

increasing

caregiver.

alert,

by

additional

the

In

social

case

caregiver

notified.

measuring

piece

On

blood

the

is that

sugar

actions.

9

2

Vision

2.1 Overall vision on social agents Our

collective

vision

derives

is

happening

from the team members’ individual

of

the

design

behaviour

experiences.

All

group

agent

through from

learning

the

according

to

user’s his

or

members are industrial design (ID)

her input to the agent. The user

master students and have their

should be able to customize the

own personal preferences when it

use of the social agent, making

comes to design. These personal

it

preferences were discussed and

social agent should not behave

weighed against each other, upon

the same in every situation, but

which

rather, it has to be context-

a

vision

towards

social

a

personalised

and

agent.

context-specific

The

agents was formed. This broad

aware

to

vision on social agents was then

prevent it from being a nuisance

made explicit with respect to our

to the user and its surroundings.

own design vision. Our vision on

An effective way to prevent any

social agents can be summarised

form of nuisance is subtleness.

in the following sentence:

This can be achieved by utilizing low volume notifications or the

“Social agents should be adaptive,

restriction of intrusive means

personalized, context specific,

of notification. The social agent

subtle

should promote social interaction

mediators

that

promote

social interaction and learn from

between

users

in

a

way

that

the users.”

it does not draw attention to itself, but attention is rather

Adaptivity

implies

that

the

device should adapt to the user, not the other way around. This

12

on the interaction between the users.

2.2 Product oriented vision By

applying

this

vision

to

the

interaction between the parent and

team’s concept - a bracelet that

the child, whilst learning from the

helps diabetic children in coping

child’s behavioural patterns when

with their condition- our vision

the aforementioned interaction is

takes a more explicit shape:

needed. See the chapter ‘Concept’ for more detailed information. A

The bracelet is rendered into an

list of nine criteria is formulated

adaptive,

to

personalized,

context-

specific, yet subtle mediator. A mediator

that

promotes

shape

the

concept

into

the

desired direction.

social

13

3

Design Ideation

3.1 Cognition To

notion

the optic array, the ambient optic

‘cognition’, we briefly describe

array, and invariant information.

what

encompasses.

These different perspectives are

Cognition (coming from the Latin

important in defining approaches

verb

is

and in understanding these mental

to

processes,

an

understand

the

cognition cognoso;

to

important

understand

perceive)

phenomenon when

explaining

as

they

offer

the

agent different points of view.

intelligent behaviour in social agents and their context as it

With

historically contributed to each

behaviour in technical concepts

other’s

and solutions (agents), we aim

research[2]. Cognition

describes of

mental

collecting,

process

processing

understanding

anything

people

experience

think,

acquire Both

through

their

consciously,

establish

something

in

a

and

similar direction to the learning

that

curve that humans have, through

and

hardware (sensing and actuating)

senses.

learning

algorithms.

These

subconsciously. It is an important

environment as people, and people

part

learning.

interact

Existing knowledge serves as a

Learning

basis

agents

for

everyday cognitive

generate

new

well

and

agents act and sense the same

of

as

to

intelligent

as

to

16

the

embodying

processes knowledge,

with

these

algorithms to

agents.

allow

develop

the

autonomous

behaviour to be able to cope with

describing a virtuous circle. In

the

this, the societal context plays

human

a big role as it serves as an

behaviour

environment in which different

contexts can not be predicted

perspectives

and/or programmed, making these

and

necessary.

are An

possible, example

is

Gibson’s theory on perception [3] described mainly in the form of

complexity

of

behaviour. in

natural

and

The

intended

these

complex

algorithms paramount.

There

are

many

variations,

The

theories and fields of application

the

within

enactive

sufficient extent to understand how

cognition,

our concept classifies as a social

cognition in psychology, etc.), but

agent (i.e. displaying intelligent

we will not go deeper into them.

behaviour) in social contexts.

cognition

cognition,

(e.g.

embodied

aforementioned concept

of

elucidates

cognition

to

a

17

3.2 Concept The concept consists of three

The high sensitive sensors are

connected

the

a result of an increasing need

bracelet itself, the detachable

for the diabetic patient to gain

add-on, and the application which

a better understanding of the

monitors

values from their glucose tests.

components;

and

tracks

the

vital

signs from the bracelet.

Extra bodily measurements will

In combination, they provide a

ensure an easier understanding

secure and subtle mediator for

the body’s reaction to food and

handling the child’s condition. In

exercise [5]. Mbrace is, worn by

most type 1 diabetes diagnoses,

the child during both day and

the

night,

person

must

adapt

to

a

constantly

monitoring

different lifestyle by changing

their heart rate variability, skin

their

conductivity

routine

towards

the

and

temperature.

inclusion of regular glucose level

Nevertheless, it does more than

tests and insulin injections, at

just base its behavior on the

a

aforementioned

input

several times a day. A syringe

These

functions

and blood glucose meter device

derived from a list of symptoms

are their primary tools to manage

that diabetics exhibit [6].

frequency

of

what

could

be

bodily

values. are

their diagnosis [4].

18

On

the

Therefore, the need for Mbrace

is

an

is apparent, as it covers the

being of the child which can be

difficult aspects of living and

adjusted

coping with diabetes, yet still

action

results

ensures a life of activity and

system

that

spontaneity,

qualitative data by remembering

aspects

that

disease often prevents.

the

the

bracelet indicator by

for

there

the

him/herself. in

a

variation

notifications.

well This

learning

incorporates

adjustment

future

itself

Once

the for an

indication of high or low blood

19

sugar occurs, a following action

available to handle these important

must

situations,

take

place.

Mbrace,

then,

therefore,

an

pushes a notification to the child

application is given to the parents,

by utilising light and vibration.

which directly links to Mbrace and

Meanwhile, the Add-on -which has

its database. The social link it

been given to a caregiver in that

creates provides ease of mind to

given

parents, by knowing that through

context-

gets

notified

as

well.

the intuition of Mbrace, difficult situations

can the

be

avoided.

The add-on ensures that the child’s

Moreover,

condition is monitored and that

the parents to monitor, plan and

the blood test - which must be

take preventive measures. All in

performed at certain times during

all, Mbrace decreases difficulties

the day - is performed together

of dealing with diabetes both as an

with a person they trust. In some

individual and as a family.

cases, the parents might not be

20

and

application

allows

21

3.3 Design rationale A

diabetes

be

for rejection. The limitations of

overwhelming; both the children

these alternatives led the team

and the caregivers go through

to refocus and redesign a trifold

a

to

system which could correspond to

things[7].

the specific needs of the chosen

rough

the

adjustment

new

Diabetic age

diagnosis

order

period

of

children

require

could

of

regular

caregiver

supervision

adjusted

lifestyle

a

young

target group.

parental/ an

For the final product, the team

stay

aspired to alleviate the issue,

consistent with care. In brief,

primarily, in terms of indicating

what the team attempted to solve

the most suitable instance for

was to reduce the difficulties

performing

that

children

Secondary, the proposed device,

and their parents have to deal

through the learning algorithm,

with during their daily routine.

creates

a

By

ensure

better

newly

difficulties

to

diagnosed

introducing

and

Mbrace,

are

these

minimised

in

the

sugar

database

symptoms

tests.

that

treatment in

the

will of

future.

a degree that can mitigate the

In parallel, it has the ability

transition phase for all involved

to

parties.

prevent

predict

and,

the

complications

22

level

therefore,

life-threatening and

outcomes

in

Ideas such as a ring wearable, a

case of a delayed (or absence

plush toy and a wearable vest were

thereof) treatment reaction. The

considered and rejected due to

collected

either their indiscreet / invasive

the pool of knowledge of type

design. Also, their inability to

1

record

of

this contribution to the field,

data or their shape inefficiency

patterns which could be applied

for this type of social agent

to every patient can be detected.

were deemed important motivators

Moreover, the families can make a

continuous

streams

data

diabetes

contributes

treatment.

to

Through

better simplified planning of their

she might not have any experience

lifestyle

with this disease. Moreover, a bond

based

indications.

on

is created between the child and

as

an

the caregiver since the wearable

additional level of safety, thus,

bracelet and the add-on are two

provide peace of mind, relief and

products

mental support to all parties.

Lastly,

can

be

a

device’s level,

Mbrace

On

the

mental

perceived

combined the

software As

mentioned

before,

the

into

mobile

enables

one.

application

the

parent

to

team

know the condition of the child at

decided to design three connected

all times, but also tracks patterns

components a wearable, an add-on,

of behaviour which aid the parent

and an mobile application software

which is not yet acquainted with

(mobile app).. This decision was

the lifestyle changes for type 1

collectively made for the following

diabetes patients.

reasons. Firstly, a wearable in a bracelet form is a versatile device

A

which would be effortless for the

Mbrace

child to wear at all times, would

solution to ease the adjustment

not attract that much attention

period

but also would not require the help

children, with benefits extending

of an adult to wear it. Secondly,

to

by using detachable add-on device,

patients. That’s what the team’s

the

aspiration

caregiver

-

responsible

in

device

with

could for

other

the

potential

become recently recently

is

about,

the

of

future

diagnosed diagnosed after

all;

the context to the child - can

designing a mediator/platform that

monitor his or her condition and

promotes social interaction.

act upon any change even if he or

23

4

Prototype

4.1 Physical prototype For the elective, the bracelet was

as

made in the form of a functioning

other variables. For this, copper

prototype. The application and

coins were used to measure skin

the

resistance,

add-on

remain

conceptual

parts complementing the concept.

To

embody

bracelet

the

assists

of

null

measurement

depending

to

on

the

skin

humidity [8].

a

diabetic

- Heart rate to give an estimate

children into a working prototype,

to the well being and/or physical

several

activity of the patient, essential

Firstly,

that

concept

a

choices we

will

were

be

made. the

when measuring high blood sugar

hardware that is used to collect

levels. A heart rate sensor is

input variables for the learning

used that utilizes infrared to

algorithm.

measure pulse in the earlobe [9].

The

describe

hardware

(i.e.

sensors and actuators) was picked by looking at the symptoms that

- Body temperature. Research has

are

diabetes

proven that the body temperature

and what symptoms are feasible

drops when the patient has a high

to collect. This resulted in the

blood sugar level, and thus low

following sensors for constant

insulin levels, due to the fact

data collection:

that insulin acts as heating for

associated

with

the body. The body temperature

26

- Skin conductance to give an

could

therefore

estimate of the physical activity

indication

the patient is having, it acts

levels [10].

of

give

the

a

good

blood

sugar

27

4.2 Electronics To support the input data, we

or high at these moments (i.e.

also collect the date and the

different from usual patterns)

time of day in the form of a

the system should warn the user

timestamp.

nonetheless.

the

This

system

to

will

predict

help future

behaviour and find patterns in

The

the daily life, such as moments

the

of

Saturday

collected sporadically. This is

and

moments

measured using a built-in blood

(e.g.

evening

sugar meter in the add-on. With

dinner) where discrepancies are

all additional data, the system

sequacious. This will help the

aims to predict the blood sugar

system

patient’s

level of the patient. By using

lifestyle and avoid false alarms

blood sugar levels as input for

at

the

activity

morning of

(e.g.

swimming)

food

to

these

intake

learn

the

moments.

However,

if

the BSL continues to remain low

28

most

important

blood

learning

system

will

sugar

data level)

algorithm, learn

to

(i.e. is

the

predict

the with

estimated

blood

increasing

sugar

accuracy.

level

the classes that needs to be defined

These

for the learning algorithm.

estimated blood sugar levels are displayed to the patient in the

As a representation of the bracelet

form of an RGB-LED that colours

and

green (i.e. perfect) to red (i.e.

sketch had been created. The sketch

either low or high BSL) with small

displays the 9 different classes

increments, and a physical slider

(in the form of digital buttons)

that

neutral

that the Matlab file calculates and

position (i.e. in the middle) to

outputs the selected class to an

either the left (i.e. high BSL), or

Arduino. The Arduino converts the

to the right (i.e. low BSL). Also,

class into output in the form of an

the caregiver and/or the parents

RGB-LED and a vibration motor. To

receive

real-

supplement the performed actions

time BSL respectively via an add-on

in the Processing sketch, a virtual

and an application as described in

slider

the chapter ‘Concept’. The add-on

slider on the bracelet, provided

and application are described in

with the LED colour together with

more detail in the chapter ‘Mobile

the estimated blood sugar level.

Application Software and Add-on’.

The full Processing and Arduino

The

sketch, together with the interface

slides

the

colour

from

its

estimated

of

the

LED

and

and

the

position of the slider represent

its

interface,

represents

a

the

Processing

physical

can be found in the appendix.

29

4.3 Data Analysis A supervised learning algorithm

indicate state 1 when the blood

will be used in combination with

sugar levels are abnormally low,

a reinforced learning algorithm

where an unsupervised learning

to make the system increasingly

algorithm could place this value

efficient.

“if-then�

in state 9 or 1, due to the fact

statement-based program is not

it is not told what it should

applicable,

fact

do. The child can indicate their

that blood sugar levels amongst

state of well being, by manually

diabetics

moving

sugar

A

normal

due

to

differ

levels

are

the and

not

blood

the

slider

to

a

more

absolute

appropriate value that represents

measurements of the current state

their state more accurately. This

of a patient [11]. Therefore, the

way the system will be trained

system needs to learn from the

in

patient, in this case the child,

learning algorithm. For example,

what normal blood sugar levels are

the system estimates the child is

and what are not. For example,

in state 8, but the child feels

a blood sugar level of 180 is

fine and consequently sets the

considered high, but not during

system in state 5. The system

lunch time, due to the fact that

will receive this as input 5 and

the patient has consumed food.

add more weight to this value

representing

a

reinforced

than the other values, resulting Moreover, operate

the with

system

cannot

in an adjusted output.

predetermined

values, the system will be trained

30

through an input interface on the

Variables

bracelet. The chosen algorithm is

The algorithm uses six variables,

therefore a supervised learning

some

algorithm. A supervised variable

weights

will be the height of the blood

variables body temperature, heart

sugar levels. The system should

rate variety, skin conductivity

of

which in

the

have

different

algorithm.

The

and heart rate variety are less

conductivity.

important

is measured using a thermometer.

sugar

than

levels

being.

measured

and

blood

state

of

the

state

Therefore,

well

Because

the

Body

temperature

variance

in

values

of

is too small to be of significant

well being and measured blood sugar

influence on the algorithm it is

levels have a higher weight than

mapped from 0 to 100 [10]. Heart

the other variables.

rate variety is measured over time, whereafter it is mapped in a class

Algorithm

from 90 to 180 [9]. This pattern

A supervised neural gas learning

could also be evaluated by a neural

algorithm

for

gas class finding algorithm, but

finding classes in sets of data.

in our case it is a matter of

The classes are presented as six

highest subtracted by the lowest

distinctive

measured

is

very

suitable

variables

that

make

heart

rate

(i.e.

heart

one estimation over the state of

rate variety). Blood sugar levels

well being of the child. All the

differ from lowest, approximately

variables

50,

are

mapped

on

certain

to

highest,

approximately

scales, displayed in the diagram

200. These values are not mapped

below. Skin conductivity is mapped

as they need to give an actual

by the sensor that measures this

depiction of the situation. Lastly,

value, being on a scale of 0 to

the state of well being is only

1023

than

presented as an additional value

262 were found, making this the

if the child manually alters the

lowest, predefined value for skin

slider to a state different from

[8].

No

values

lower

31

the suggested class output. If

data

is

presented

to

system,

the state of well being were only

resulting in the output as shown

input, whilst being an output,

in the image below. The data set

the system will become faulty.

consists of 1222 pairs, which are

The time of the day is only used

all mapped in different states

as a prediction for the system

of well being, showing that the

and cannot have weight added to

system is working.

it.

Reinforced learning Training the algorithm The

32

algorithm

is

trained

The system is initially calibrated in

through

supervised

Matlab using 100 sets of data.

However,

The 100 sets of data resemble

variables and classes have been

the variable values as presented

defined, the child teaches the

above and is used to calibrate the

system

system. 9 classes are indicated

reinforced

learning.

as output and after the system

difference

between

is

and reinforced learning is the

calibrated,

the

collected

after

learning.

how

to

the

system

behave

through

The

main

supervised

feedback loop in the system, which we

An example

use to our advantage. In reinforced

In this example, displayed in the

learning the system learns through

image below, the child has just

reward and punishment, where in

woken

supervised

system

presented in minutes, making it

learns through predefined rules.

08:00 in the morning at the first

The

reinforced

set of data. The body temperature is

learning, because blood sugar levels

normal, as is the skin conductivity

differ amongst diabetics, making

and heart rate variety. The blood

it mandatory to have personalized

sugar levels are not exactly what

adjustments in the system. These

they should be, since the child

personal

learning

system

requires

adjustments

predefined,

as

would

the

up.

The

time

of

day

is

cannot

be

has not had food for a long period

happen

in

of time whilst being asleep. The

supervised learning, but need to

system

will

be set through the aforementioned

state of well being in class 4,

feedback loops.

a

relatively

therefore normal

place

the

situation.

The child eats something (time of

33

day 450 minutes) and the values

Application

are back to normal. After some

For children with diabetes being

time

connected

the

child

will

take

the

short workout (time of the day

knowing that the parents and the

510 minutes). During this period

responsible caregiver can react

the body temperature, heart rate

as soon as the children would feel

variety

distressed.

skin

conductivity

the

parents

could

and

give

their

bicycle to school, which is a

It

children

is

ease,

accomplished

will slightly rise and the blood

by providing the parents with an

sugar levels will drop. This is a

application, as well as an add-

potentially dangerous situation,

on from the bracelet that are

which is why the system will place

given to the responsible adult

this data set in state 3. The

in a given context. Whereas, the

child knows it has a low blood

add-on signals in case action is

sugar level and as a result will

needed, the application provides

take some sugar, after which the

data and monitorial options as

values will go back to normal.

well as planning.

of

Add-on The

34

with

what

procedure

to

follow.

In cases of high or low blood

add-on

functions

through

sugar levels a glucose test is

light indications and vibrations.

performed with a syringe and the

It

bracelet’s

add-on that functions as a blood

notifications for when an action

glucose meter. Depending on the

is

the

readings and the LEDs colour on

caregiver

the bracelet and add-on insulin

mimics needed.

light

the The

informs

colour the

of

or sugar are given to the child.

its upper side. The battery within

Visually the add-on is a cylindrical

is

disc with a light emitting plus on

during the night.

recharged

through

induction

35

4.4 Application The application

data, the calendar, food plan and

Social and subtle communication

a predictor.

requires

be

carefully

and

securely handled when it comes

Start screen

to

The

continuous

data

streams.

need

for

the

with only two phones. This allows

of their child is fulfilled by

for

security

adding a rotating circle in the

and direct contact between the

centre of the screen where values

parents and their child. While

in

the bracelet itself is used as

the change in wellbeing in the

an agent in the background, the

last

application

giving

the circle the numeric value is

the parents ease and critically

displayed on the opposite side

review

of

high

level

of

serves

the

in

effectiveness

and

different hour.

the

In

menu

the

to

immediately

a

see

parents

Therefore, the bracelet is paired

colours

wellbeing

indicate

connection

button.

with

In

the

accuracy of the agent. Moreover,

lower right corner an icon for

the

important

taken blood tests are displayed.

information of the child’s status

The number of the drop icons

but have only few measures of

resembles the number of times

notifying

the test was performed

the

parents

or

obtain

“talking�

application,

through

which

serves

the goal of providing the child

- The circle shows the data sent

more independence and control of

from the bracelet from the last

their own situations.

hour

The a

36

to

application

start

screen

consists with

built

as

well

as

the

current

of

Glucose level. the colors depict

in

the change in glucose levels.

menu. In the menu, a list of

- In the bottom right is an icon

four

representing the number of blood

trackers

disabled.

The

can menu

be

enabled/

allows

the

user to navigate to the tracking

tests the child has performed during the day.

Menu screen - The menu where tracking analysis and other functions can be selected.

- In the bottom right the indicator of

taken

blood

tests

are

still

visible.

menu Screenfigure

Data tracker The the

data

wristband

represented bars,

streams

color

to

sent

the

through coded

from

phone

graphs with

is and

lowest

values in blue and highest values in yellow. While this service is not vital for most users, a history of the variety can help to better plan and understand the necessary countermeasures

for

the

parent

and children to take. Especially, for families where diabetes is a new factor on which they have to structure their lives around.

Start Screen figure

37

Patient’s stats figure

Calendar figure

- Of four possible trackers the

particular situation is accounted

user

for.

can

view

the

graphical

representations of the different inputs the bracelet collects.

- The calendar allows the user to

- The graphs further indicates

inform the system of particular

if the values are stable.

days that includes extra exercise or intake of sugar for the child.

Calendar

38

The calendar and food plan allows

Predictor

the

events

The prediction system functions

that require extra preparation.

in two ways. It visualizes the

This is accomplished by dragging

accuracy

a circle onto one of the dates

system

in the month. This serves as an

reinforcement

indicator that on this particular

contributes to the critical view

day the parent should ensure the

the parents should have towards

family

to

schedule

of as

it

the

intelligent

learns learning,

through which

these systems. It is due to the chance

of

errors

that

they

by

default shouldn’t completely trust the

validity

of

the

data

they

obtain. Therefore, offering a value that describes the accuracy of the system benefits the users in being reactive as well as critical where their own intuition also counts.

The application further offers a prediction tool in which the system predicts the glucose levels of the children in a given timeframe. This allows the parent and child to take preemptive measures, in respect to the accuracy level.

Predictor figure

39

5

Scenarios

Scenario 1: High blood Sugar levels

In this scenario, which is most likely to occur after a meal, there is a peak of the patient’s blood sugar levels. The device detects the concerning patterns of data. The slider value adjusts accordingly to the appropriate class, in this case state 2. The device communicates the severity of the situation by utilising the vibration motor and the integrated LED lights.

The caregiver receives the appropriate notification through the add-on and the data is sent to the mobile application software. The child has the ability to decide whether to reject or approve this indication; by altering the position of the slider. The caregiver is further notified of the condition of the child at the same levels and is advised to perform a blood sugar test with an indication for a possible insulin injection. If action is performed, the slider returns to the initial position, state 5.

42

Scenario 2: Low blood Sugar levels

In this scenario, the child has the bracelet and a caregiver, which has the bracelet add-on, is within close vicinity of the child.

During the

break the child engages in high activity, resulting in a drop of blood sugar levels. As a result, the device detects concerning patterns of data. Following this, the slider value adjusts to the appropriate class, in this case state 8. The device, once again, communicates the severity of the situation by utilising the vibration motor and the integrated LED lights. The caregiver receives the appropriate notification as well as on the add on. As the previous scenario, the child has the ability to decide whether to reject or approve this indication; by altering the position of the slider. The caregiver is further notified of the condition of the child at the same levels and is advised to perform a blood sugar test with an indication for a possible sugar intake.

If action is taken, the

slider returns to the normal position, state 5, whereafter the child may continue his/her play.

43

- Scenario 3: Add-on tracker

The child hands the add-on tracker to the caregiver which will supervise his or her following activity. The caregiver will provide the appropriate action, if needed.

44

6

Discussion

In this section the team would

State of well being

like to critically address the

The states of well being are used

aspects

to

of

the

concept

and

train

the

algorithm.

These

practice - by utilizing algorithms

states

-

adjusted by the child in case

which

evidently

shaped

the

should

be

indicated/

design process and focus during

the

device

provided

a

false

the product development.

prediction,

something

a

child

is unable to understand during

Unsupervised

their

learning algorithm

with the bracelet. The concept

The learning algorithm used is a

itself

simulation of the real situation.

that the algorithm can learn in a

The used algorithm is the provided

more accurate way. Furthermore,

unsupervised learning algorithm,

according to the goals of the

whereas the actual algorithm will

elective,

be a combination of supervised and

all

reinforced learning algorithms.

possesses different weights as

With

the

unsupervised

algorithms,

values cannot be categorised nor

of

placed into their assigned state.

the

familiarisation should

be

the

aspects

adjusted,

integrity

of

the

understanding the

technology

others.

It

period so

of

concept

and

vision

overshadows

was

therefore

paramount that the team focused

Variables

on

To save unnecessary wasted time,

algorithm and desired behavior

the amount of variables is kept

instead of aiming to develop the

at four, instead of the actual

perfect concept.

six.

The

focus

qualitative

high

was and

on

the

of

the

making

accurate

Two

way

communication

App-

variables, not on making producing

bracelet and add-on-bracelet

the exact amount of variables.

In terms of social communication through

46

development

intelligent

artifacts,

it was decided that a one way communication from Mbrace were preferred. Mainly two points were

used for this decision, namely, that

push a glucose test request through

the learning process and change of

their app, because they felt it was

lifestyle of the child should be

necessary, the situation might be

done gradually towards the child

interpreted completely differently

becoming

independent

by the child. Therefore, a one way

from their parents. Secondly, the

communication seemed appropriate

meddling

to maintain the simplicity of the

somewhat from

external

parties

could lead to interpretation errors

outputs from the bracelet.

from the system. For example if the parent could have the option to

47

7

Evaluation

This

assignment

by

five

was

performed

was made to put an extra person

Design

on this task. After making use of

(ID) students, who all had no

different algorithms from online

prior experience with learning

sources, plus using the provided

algorithms

algorithm,

Industrial

or

even

Matlab.

there

were

little

Some programming knowledge was

results. The provided algorithm

present,

was unable to function due to an

but

applicable

this

error in the program, something

five ID students had knowledge

we were unable to detect. When

in making concepts and working

the error was finally resolved by

them out, which is why the first

dr. ir. E. Barakova, we were able

few weeks the concept was very

to continue and received results

quickly formed. After the first

rapidly. Partly due to the error

few

that took too long to resolve,

of the

the

Matlab.

hardly the

weeks,

to

was

when

concept

algorithm

All

the was

could

outline finished,

were

unable

to

implement

chosen

the correct learning algorithm.

and made ready for use. It was

Possibilities to have the correct

chosen to have one person as the

learning

main

choosing

been present, if the time could

and working with the algorithm,

have been used efficiently. On the

while the other members gathered

other hand, the group dug deeper

and prepared the data for the

into

algorithm (amongst other tasks).

about learning algorithms due to

responsible

for

be

we

the

algorithm

existing

could

have

knowledge

the error, which caused for an One person on the algorithm turned out to be too little, which is why the last two weeks the choice

50

instructional experience.

8

Conclusion

During this elective it became

The

clear

to

good starting point to learn how

design for social behaviour, and

to use them. Also, MatLab is a

that it is even more difficult to

program that works very well with

program it. Algorithms are very

algorithms and data visualisation.

useful

that

in

it

is

difficult

creating

algorithm

provided

was

a

an

overall

It did require some trial and

the

social

error to get it to work properly,

agent,

but it was a useful part of the

as became apparent during the

learning process. MatLab proved

course of this elective.

to be a powerful tool for quick

‘understanding’ situation

of

within

the

and clear data analysis. Future The course revealed the relevance

application of MatLab is probable

and need of algorithms to the

when dealing with complex data

team. Algorithms make a system

computing in the future.

self-adjustable

to

different

situations without the need of

Everyday non intrusive products

very complex programs. Algorithms

need to be subtle. When something

are generally applicable, in a

needs

way that the data testset will

to make it’s presence known in

inform the algorithm about the

natural, yet understandable ways;

data

and

which

used

in

for

the

the

output

specific agent.

can

be

programming

But

there

is

to

is

be

subtle,

sometimes

it

needs

tricky

for

an inorganic thing. A lot needs to

happen

at

once.

Here

the

no need for most situations to

ability of an algorithm to learn

create

of

a

dedicated

algorithm.

previous

and

new

data

and

The algorithm helps systems to

situations is of key importance.

self-adjust and create safer and

With new input it adjusts more

better solutions for the users.

and more to the user, fitting accordingly in the individual’s everyday life.

52

9

References

[1] Type 1 Diabetes. American

Mayoclinic.org, 2015. http://

diabetes Association, 2016.

www.mayoclinic.org/diseases-

http://www.diabetes.org/diabetes-

conditions/diabetic-coma/basics/

basics/type-2/?loc=db-slabnav.

definition/con-20025691.

[2] Wilson, R. and Foglia, L.

[7] Azar, R. and Solomon, C.

Embodied Cognition. Plato.

Coping strategies of parents

stanford.edu, 2011. http://plato.

facing child diabetes mellitus.

stanford.edu/entries/embodied-

Journal of Pediatric Nursing 16,

cognition/.

6 (2001), 418-428.

[3] Gibson, James J. The

[8] Sweat meter could alert

Perception of the Visual World.

diabetes patients about low

Boston: Houghton Mifflin, 1950.

blood sugar. News-Medical.net,

Print.

2011. http://www.news-medical. net/news/20110831/Sweat-meter-

[4] Checking Your Blood Glucose.

could-alert-diabetes-patients-

American Diabetes Association,

about-low-blood-sugar.aspx.

2015. http://www.diabetes.org/ living-with-diabetes/treatment-

[9] Schroeder, E., Chambless,

and-care/blood-glucose-control/

L. and Liao, D. et al. Diabetes,

checking-your-blood-glucose.

Glucose, Insulin, and Heart Rate

html.

Variability: The Atherosclerosis Risk in Communities (ARIC)

[5] Hi-tech devices for self-

study. Diabetes Care 28, 3

monitoring your diabetes |

(2005), 668-674.

beating-diabetes.com. Beating-

54

diabetes.com, 2014. http://

[10] Body temperature can have

beating-diabetes.com/index.

profound impact on diabetic

php/hi-tech-devices-for-self-

patients. News-Medical.net,

monitoring-your-diabetes/.

2016. http://www.news-medical. net/news/20160401/Body-

[6] Diabetic coma - Mayo Clinic.

temperature-can-have-profound-

impact-on-diabetic-patients.aspx.

[11] Blood Sugar Levels for Kids and Teens. WebMD, 2014. http:// www.webmd.com/diabetes/type-1diabetes-guide/normal-blood-sugarlevels-chart-kids-teens.

55

10

Appendices

Processing Sketch // All Examples Written by Casey Reas and Ben Fry // unless otherwise stated. import processing.serial.*; HScrollbar hs1;

// Two scrollbars

color currentcolor; RectButton rect1, rect2, rect3, rect4, rect5, rect6, rect7, rect8, rect9, rect10; String[] classState = {“BSL > 240”, “BSL 180”, “BSL 130”, “BSL 115”, “BSL 100”, “BSL 80”, “BSL 70”, “BSL 60”, “BSL < 50”}; int sliderY; int height = 1000; int width = 500; int textval = 4; int X = 100; int Y1 = height/10-25; int Y2 = (height/10)*2-25; int Y3 = (height/10)*3-25; int Y4 = (height/10)*4-25; int Y5 = (height/10)*5-25;

58

int Y6 = (height/10)*6-25; int Y7 = (height/10)*7-25;

int Y8 = (height/10)*8-25; int Y9 = (height/10)*9-25; int sliderpos = Y5; int slidercolor = color(51,255,51); boolean locked = false; Serial port; void setup() { //hs1 = new HScrollbar(300, height/2, width, 16, 16); hs1 = new HScrollbar(300, 50, 50, 900, 3*5+1); size(700, 1000); smooth(); color baseColor = color(102); currentcolor = baseColor; color buttoncolor = color(204); color highlight = color(153); buttoncolor = color(255, 51, 51); highlight = color(255, 51, 51); rect1 = new RectButton(X, Y1, 50, buttoncolor, highlight); buttoncolor = color(255, 153, 51); highlight = color(255, 153, 51); rect2 = new RectButton(X, Y2, 50, buttoncolor, highlight);

59

buttoncolor = color(255, 220, 51); highlight = color(255, 220, 51); rect3 = new RectButton(X, Y3, 50, buttoncolor, highlight); ....etc. port = new Serial(this, “COM12”, 9600); } void draw() { background(220); noStroke(); hs1.update(); hs1.display(); stroke(0); fill(255); noStroke(); rect(75, 50, 100, 900, 10); textSize(24); text(classState[textval], 400, 505); update(mouseX, mouseY); rect1.display(); rect2.display(); ....etc. }

60

void update(int x, int y) { if (locked == false) { rect1.update(); rect2.update(); ....etc. } else { locked = false; } if (mousePressed) { if (rect1.pressed()) { currentcolor = rect1.basecolor; port.write(‘9’); textval = 8; sliderpos = Y1; slidercolor = color(255, 51, 51); } else if (rect2.pressed()) { currentcolor = rect2.basecolor; port.write(‘8’); textval = 7; sliderpos = Y2; slidercolor = color(255, 153, 51); } else if (rect3.pressed()) { currentcolor = rect3.basecolor; port.write(‘7’); textval = 6; sliderpos = Y3; slidercolor = color(255, 220, 51); } else if (rect4.pressed()) { currentcolor = rect4.basecolor; port.write(‘6’);

61

textval = 5; sliderpos = Y4; slidercolor = color(153, 255, 51); } else if (rect5.pressed()) { currentcolor = rect5.basecolor; port.write(‘5’); textval = 4; sliderpos = Y5; slidercolor = color(51, 255, 51); } else if (rect6.pressed()) { currentcolor = rect6.basecolor; port.write(‘4’); textval = 3; sliderpos = Y6; slidercolor = color(153, 255, 51); } else if (rect7.pressed()) { currentcolor = rect7.basecolor; port.write(‘3’); textval = 2; sliderpos = Y7; slidercolor = color(255, 220, 51); } else if (rect8.pressed()) { currentcolor = rect8.basecolor; port.write(‘2’); textval = 1; sliderpos = Y8; slidercolor = color(255, 153, 51); } else if (rect9.pressed()) { currentcolor = rect9.basecolor; port.write(‘1’); textval = 0;

62

sliderpos = Y9;

slidercolor = color(255, 51, 51); } } } class Button { int x, y; int size; color basecolor, highlightcolor; color currentcolor; boolean over = false; boolean pressed = false; void update() { if (over()) { currentcolor = highlightcolor; } else { currentcolor = basecolor; } } boolean pressed() { if (over) { locked = true; return true; } else { locked = false; return false; }

63

} boolean over() { return true; } boolean overRect(int x, int y, int width, int height) { if (mouseX >= x && mouseX <= x+width && mouseY >= y && mouseY <= y+height) { return true; } else { return false; } } } class RectButton extends Button { RectButton(int ix, int iy, int isize, color icolor, color ihighlight) { x = ix; y = iy; size = isize; basecolor = icolor; highlightcolor = ihighlight; currentcolor = basecolor; } boolean over()

64

{

if ( overRect(x, y, size, size) ) { over = true; return true; } else { over = false; return false; } } void display() { noStroke(); fill(currentcolor); rect(x, y, size, size, 5); } } class HScrollbar { int swidth, sheight; float xpos, ypos; float spos, newspos; float sposMin, sposMax;

// width and height of bar // x and y position of bar // x position of slider // max and min values of slider

int loose;

// how loose/heavy

boolean over;

// is the mouse over the slider?

boolean locked; float ratio; HScrollbar (float xp, float yp, int sw, int sh, int l) { swidth = sw; sheight = sh; int heighttowidth = sh - sw; ratio = (float)sh / (float)heighttowidth;

65

xpos = xp-swidth/2; ypos = yp; spos = ypos + sheight/2 - swidth/2; newspos = sliderpos; sposMin = ypos; sposMax = ypos + sheight - swidth; loose = l; } void update() { //if (overEvent()) { //

over = true;

//} else { //

over = false;

//} if (mousePressed) { locked = true; } if (!mousePressed) { locked = false; } if (locked) { newspos = sliderpos; } if (abs(newspos - spos) > 1) { spos = spos + (newspos-spos)/loose; } } float constrain(float val, float minv, float maxv) { return min(max(val, minv), maxv);

66

}

boolean overEvent() { if (mouseX > xpos && mouseX < xpos+swidth && mouseY > ypos && mouseY < ypos+sheight) { return true; } else { return false; } } void display() { noStroke(); fill(204); rect(xpos, ypos, swidth, sheight, 10); if (over || locked) { fill(slidercolor); } else { fill(slidercolor); } rect(xpos, spos, swidth, swidth, 5); } float getPos() { // Convert spos to be values between // 0 and the total width of the scrollbar return spos * ratio; } }

67

Arduino Sketch const int dcMotor = 5; const int red = 11; const int green = 10; const int blue = 9; const int GSR = A0; const int sliderValue = A2; int threshold = 0; int skinValue; int slider; int motorValue; int counter = 0; int counter2 = 0; int prevskinValue = 0; int addedskinValue = 0; int serialskinValue = 0; int incomingByte;

// a variable to read incoming serial data into

void setup() { // put your setup code here, to run once: long sum = 0; Serial.begin(9600); pinMode(sliderValue, INPUT); pinMode(GSR, INPUT); pinMode(blue, OUTPUT); pinMode(green, OUTPUT); pinMode(red, OUTPUT); pinMode(dcMotor, OUTPUT);

68

for (int i = 0; i < 500; i++)

{ skinValue = analogRead(GSR); sum += skinValue; delay(5); } threshold = sum / 500; } void loop() { // put your main code here, to run repeatedly: int temp; skinValue = analogRead(GSR); slider = analogRead(sliderValue); addedskinValue = skinValue + prevskinValue; prevskinValue = skinValue; counter++; counter2++; if(counter == 60){ serialskinValue = addedskinValue/60; Serial.print(serialskinValue); Serial.print(“ @ “); addedskinValue = 0; counter = 0; Serial.println(slider); //

Serial.println(“ : “); delay(100); temp = threshold - skinValue; if(abs(temp) > 100) {

69

skinValue = analogRead(GSR); temp = threshold - skinValue; if(abs(temp)> 100){ } } } if (Serial.available() > 0) { // read the oldest byte in the serial buffer: incomingByte = Serial.read(); // if it’s a capital H (ASCII 72), turn on the LED: if (incomingByte == ‘9’) { analogWrite(red, 255); analogWrite(green, 0); analogWrite(blue, 0); Motoron(); } else if (incomingByte == ‘8’) { analogWrite(red, 255); analogWrite(green, 51); analogWrite(blue, 0); Motorpulse(); } ....etc. } } } void Motoron() { motorValue = 255; analogWrite(dcMotor, motorValue);

70

}

void Motoroff() { motorValue = 0; analogWrite(dcMotor, motorValue); } void Motorpulse() { motorValue = 0; analogWrite(dcMotor, motorValue); delay(500); motorValue = 255; analogWrite(dcMotor, motorValue); delay(500); }

71

Matlab Sketch LoadTrain: clear; load kleinbesta2; TrainingSet_Clusters = neural_gas_cluster (kleinbesta2, 9, 1000); TestSet load besta2;

%Loads the test set

[NoOfScans, lasers] = size (besta2); NoiseChanges =5; t=0:pi/360:pi-pi/360; Scan_Prev_Index =1; ii = 0; Prev_Index = 1; NoOfClasses = 9; NewClass = 0; %%%%%%%%%%%%%%%%%%%%%%%%%%%%% %This part classifies the test set to previously clustered training set. %A plot of the belonging of the test set patterns to a particullar class is %made. The number of Classes are determined. %%%%%%%%%%%%%%%%%%%%%%%%%% for Scan = 1:1:NoOfScans Test_Set_1_class = neural_gas_class(TrainingSet_Clusters,besta2(Scan,:)); %Classifies the test vectors to the known classes figure(3);

hold on; plot(Scan,Test_Set_1_class,â&#x20AC;&#x2122;b*â&#x20AC;&#x2122;);

if (NewClass ~= Test_Set_1_class) NoOfClasses = NoOfClasses + 1; NewClass = Test_Set_1_class;

72

end; end;

Regression with own code clear; close all; load halfuur; y = 1:30; a= halfuur(:,3); s= halfuur(:,5); xlim ( [0, 36] ); hold on; plot (y,a, ‘:s’, ‘MarkerSize’, 6, ‘MarkerFaceColor’, ‘b’); title (‘Sugar’); xlabel (‘time’) ylabel (‘sugar’) y2 = transpose (y); %p = polyfit(y2,a,5); %pp = polyval(p,y); hold on; fit1 = fit(a, y2, ‘poly3’, ‘Normalize’, ‘on’); fitexp = fit(a, y2, ‘exp1’); fitexp2 = fit(a, y2, ‘exp2’); y3 = 1 : 50; plot (fit1, ‘b’); plot (fitexp, ‘g’); plot (fitexp2, ‘m’); plot(pp, ‘predobs’);

73

11

Reflections

Reflection

- Marijn Bults s154163

Knowledge beforehand When starting the elective, I had no prior knowledge of Matlab except for a small project in my first year as an ID student. I only knew Matlab was a potentially very interesting and useful application, therefore I decided I wanted to be responsible for the algorithm part in this elective. As for learning algorithms, there also was no knowledge about this section.

To say it bluntly, I had no idea where I was starting on. The field we chose, children with diabetes, is not a field my interests are in. All the mentioned aspects made this elective a good step out of my comfort zone.

What went wrong?

During the first weeks we did not start on implementing learning algorithms, we first wanted to have a strong concept and something physical to work with. I, therefore, made a SolidWorks file of a bracelet, to have it 3D printed and ready to be used. We decided upon the data we needed to collect to make a learning algorithm, after which we started on making and implementing learning algorithms in Matlab. To be able to do this, I first took a small online course for Matlab, to cover the basics, since I was the sole responsible for the learning algorithm in the beginning. The week we begun working on the learning algorithm was as a result week 4. I also immediately started looking for examples online and starting to implement them. This has been a very instructional experience, but it costed a lot of time. It would have been better if we started on the learning algorithm in week 1 and if I first wouldâ&#x20AC;&#x2122;ve used the provided learning algorithm.

In the provided learning algorithm, the most challenging part is providing usable data. I thought I had usable data, but could not get the learning algorithm to work. In the end it turned out that I had usable data, but the error was in the learning algorithm. A lot of time couldâ&#x20AC;&#x2122;ve been used better if I didnâ&#x20AC;&#x2122;t got stuck on the error.

76

What went well?

First of all it needs to be said that the teamwork overall was very good.

Everybody contributed and no one was repeatedly late with their work or for the meetings. The choice to have an additional member, Bastiaan van Hout, on the algorithm task was also a good call to make. I updated him on my matlab experiences, something that was also instructional for Bastiaan, and by explaining what I was doing I got a better feel of the system.

What would I have done differently?

I wouldâ&#x20AC;&#x2122;ve started working in matlab and on the provided learning algorithm from day 1. I would like to be able to successfully implement a supervised learning algorithm, since I think I now have the knowledge to do so. I also would like to improve the concept, since itâ&#x20AC;&#x2122;s not completely realistic to give a child so much responsibility over their diabetic situation (the child controls the slider values). All in all I think I have been a very useful member of the design, as was everyone else. The elective has been a very instructional experience, because I now have experience with Matlab and learning algorithms in general. The mentioned error I experienced in matlab that caused the system not to work, caused me to look deeper into the subject, which is why I am actually happy the error occurred.

77

Reflection

- Eleni Economidou - s150682

Motivation The reason behind my personal preference to choose a such a vulnerable target group and an ambitious concept was the experience gained from trying out a solution with social implications. Having to make a choice between the CDR and the RDD track, I have selected this elective so that I gain a better insight by working within a research design and development framework. This elective proved to be a unique opportunity to design embodied meaningful products that have the potential to create an immediate impact on society, and especially those who are in need.

Aims My personal goals and expectations out of this elective - and particularly out of designing for children with a disease like diabetes - included improving my design approach, familiarising myself with the general philosophy

of

employing

embodied

intelligent

behaviour

in

a

social

context, as well as the coding that takes place behind it. In addition, extending my skill-set by gaining further knowledge about the types of intelligent algorithms and the differences between each one, learn how to research before applying solutions, developing an understanding of how the input data is collected and analysed, and, lastly, implement concepts for meaningful social interaction using these intelligent algorithms. In short, my main goal was to adopt the most suitable mindset for a design project of this nature.

Learning activities / duties Initially, the team analysed the given paper and got acquainted with the foundation of embodied cognition. In the following weeks, the team went through the iterative process to figure out the approaching angle of the intelligent embodiment frame, researched for other examples, solutions

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and publications within the subject in question, created a low-fidelity

prototype. The process was quite collaborative, since each member of the team brought to the table a different set of competencies. On a personal level, I have gained further knowledge on what intelligent algorithms are and the difference between each type, how to design for specific scenarios in a social context, improved my persona design, editing and animation of 2D vector graphics in After Effects - something I felt confident in doing but also wanted to improve and also had a role in the documentation and the setup of the report. I have closely observed what the other team members responsibilities were and got acquainted with how intelligent algorithms work even though I havenâ&#x20AC;&#x2122;t implemented it myself.

Relevance for future development By the end of the elective there was a clear understanding of the methods of using intelligent algorithms in a meaningful way within a social context. Personally, I would like to be more active on the programming part but the team decided to separate tasks according to each memberâ&#x20AC;&#x2122;s strengths, something that proved to be a wise decision since the result would not have been the same or of the same quality. To conclude, any gained knowledge from this elective could be applied in my future design explorations, thus, contributing to existing areas of design.

Team dynamics I genuinely enjoyed being a member of this team. We came a long way, but, we were mostly efficient. The planning of tasks was successful and there was a friendly climate amongst us. Collaboration came naturally due to an immediate connection and common understanding. Everyone had his or her skills that we wanted to share in order to contribute to better results.

Iâ&#x20AC;&#x2122;m looking forward to future collaborations.

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Reflection - Ivar Kraaijevanger - s156193 At the beginning of this elective I was not sure what to expect. I knew about algorithms, but not what they were able to do exactly. The lectures helped to clarify what different types of algorithms there are and what they are capable of.

In the beginning of the elective I wanted to get our idea clear. We brainstormed a lot. We discussed

all kind of different ideas on where we

could design a solution for. A solution that had the potential to embody social and intelligent behaviour. We decided to create a solution for children with diabetes. Children are vulnerable and forget things easy. A device to help keep them keep track of their illness and keep monitoring their health can be highly beneficial for them. I 3D printed the design we made so we had a shape prototype to get things going.

The elective challenged me. It took me a while to wrap my head around the paper we were provided with at the beginning of the elective. It was a dense paper, describing about 45 years of scientific research. It was very useful to have it chopped up in pieces and presented from one team to another. I did need to read it a couple of times to really understand it.

I think we made a good start with pinning down the concept we wanted to develop. But we should have started with the algorithm earlier. Marijn struggled a lot. I tried to help him. I research on what kind of data we should use for the feed for the algorithm. Eventually Bastiaan teamed up with Marijn to get the learning algorithm working.

We thought it was useful to have an application that runs on a mobile platform such as a smartphone. This device will enable the bracelet to communicate with anyone near and far away in, for example, case of emergency. Also the phone will be able to store all the data and provide the user with insights.

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I designed the basis for the app, who then later were redesigned by Thijs.

The lay-out for the report is something I took responsibility in. I need to work on my graphic skills as a designer. This is something that lacked in my bachelor education at the Hogeschool Utrecht. Therefore I wanted to develop my graphic skills and thought this elective was a useful opportunity. I asked my team and also outsiders what they thought of the lay-out, so that I was able to develop my skills further and know what others would think of my graphic development.

The teamwork was good. Everyone worked well and I believe that the work was evenly distributed. Some did work that was more visible than others.

The team dynamics were also good, which ensured that meetings were never boring or useless. Thought they could sometimes have been more effective if we stayed on topic more. Nonetheless I am happy with the outcome of our work.

If I would do the elective again I would like to do more on the programming and the learning algorithm part. I think Marijn did a good job. He claimed the learning algorithm part, and I did not communicate clearly that I wanted to do that too. A part of the problems was that I needed to figure out how to get MatLab installed on my MacBook, while Marijn already had his laptop set up quickly with MatLab. I have learned a great deal in this elective. It see a lot more opportunities for me to program with. In hindsight of programs I tried to make I believe that I had an earlier need for algorithm than this last quartile. So I am happy to have learned the potential of algorithms and will certainly use those later on to get programs working better.

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Reflection - Thijs H. Roeleven - s144261 My interest in intelligent agents and behavior of products originates from my interest and knowledge in designing for children. In many design processes, the desire for making a product that grows whilst used would seem perfect, but is oftentimes downgraded and replaced by a simple version with limited adaptivity. However, if you succeed in making an adaptive and sensing product, you get a product that doesn’t become boring or outdated.

My motivation and goal to learn more about technology that changes society further helped me decide to choose this elective, as the area – Technology and realization – deals with creating a vision and expectation of how the future will change. A change that is perceived from a technological impact or in this case on the behavioral impact on the user.

The expectation

for what the future occupies me as a designer and it inspires me.

During the process of choosing a context for where a social agent best could be utilized, it became apparent that the vision guides this process. Therefore, the choice landed on minimizing the goal for what the agent should do. Additionally, the context that was chosen suited me very well as my interest for designing for children acts as a great motivator. The process of familiarizing myself with the theory and history of cognition / artificial intelligence is something I have touched upon before to an extend that I could refer and discuss the topic with reference to other papers, such as, the paper from 1991 – The computer for the 21st century. The paper does what I accomplished in this elective. It suggested a peek into the future and how it could look in 10-20 years time, and realized this, just as us, through mockups and prototypes.

In a team of five designers the focus on interaction with the bracelet was a priority that introduced different responsibilities to effectively

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work together. It meant that while involved in most parts, I was mainly focusing on implementing the heart rate monitor and the development of the application and add-on. It somewhat resulted in trusting people and allowing them to take charge of their own areas.

It was interesting to experience the time consuming process of approaching a project differently. To start with the attempt to add data to a learning algorithm with a hope for a meaningful result, resulted in a more chaotic progression where uncertainty for the topic and of the usage were slightly dominating. However, I feel that I and the team worked well together in solving the challenges together.

Looking back, I hope to further improve my understanding of neural network algorithms as they have gained an increasing investment from the tech industry, where especially Google is a frontrunner with their selfdriving cars and dream machine. On the other hand, I sustain my critical view on where this approach can be applied to, as privacy, ethics, morals and security for the users are challenged with the development of thinking products.

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Reflection - Bastiaan van Hout - s123868 Perspective on intelligent behaviour in social agents This elective has been important in the development of my view on the role that products should and can have in design. It has provided me with a perspective on products and how people interact with them, and on social behaviour in general. With my vision towards systems design and the importance of (societal) context, I see individual agents interact with each other (mediated) through technology. The theoretical background knowledge provided in this elective in combination with a practical solution to promote social interaction through intelligent behaviour, proved to be an effective learning trajectory. At first I was sceptical about robotics and their role in society and I still am, but I have opened up to the potential of learning algorithms and their embodiment. In my view robots can learn to perform mundane and quite complicated tasks, but they can never have genuine social capabilities.

However, when thinking about social behaviour, one could argue that if a person feels a robot is displaying social behaviour, the robot has succeeded in social interaction. With learning algorithm chat programs already leading towards characters being indistinguishable from humans, a big step in direction has already been made.

Complexity of human behaviour I do believe that the next step towards this behaviour in an embodied agent is a very big one. An important argument in this is the complexity of human behaviour and skills. Will social behaviour in interaction between humans and products/robots ever be possible? Or will they remain nothing more than social mediators for interaction among humans? And, if so, could this eventually lead to a form disconnected between people over a longer term if every form of interaction is technologically mediated? In my view, intelligent and interactive products are potentially great applications to promote

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social interaction (as long as they are mediated properly), but I am curious to see where the development of autonomous robots goes to and its impact on particularly the ethical side of human-product interaction.

Perhaps the fact that this social behaviour is taught by humans gives it some form of realness? This is something to definitely ponder about.

The elective and its role in my designs The learned algorithms and software to create learning products and systems can be very useful in my development as a designer, especially towards personalized solutions. A system can learn to adapt to the user(s) and context, and grow to fit the user(s) better and better over time.

This allows for a product to be similar at first with different users, but develop to grow into a situation that is desirable and meaningful. In the elective I mainly focused on the theories and the why, and I tried to understand the use of learning algorithms and their functionality to answer to the aforementioned insight of using them in my designs.

By implementing the learning algorithm functionality into a working prototype in combination with a visual representation of what the concept could look like we managed to communicate the concept efficiently and clearly. What I would do different in the future is to communicate the necessity of the concept in more detail in the presentation. We mainly focused on learning the algorithm and its use, and how to communicate its use through detailed scenarios. However, to convince the audience of the necessity, a clear description and proof for the concept to be meaningful and desirable is paramount. In short, the elective has given me useful and important insight into the potential and use for these systems, and it made me contemplate about where and how these systems can be of value.

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