Paths of Wellbeing onSelf-Organizing Maps

Krista LagusTommi VatanenOili KettunenAntti HeikkiläMatti Heikkilä Mika PantzarTimo Honkela

Aalto University(former Helsinki University of Technology)

Sports Institute of Finland

Stressinmurtajat

National Consumer Research Center

Finland

Motivation for Wellbeing informatics

• World health situation:

• WHO alarms of a stress epidemic: top 5 debilitating diseases are related to stress

• Challenge: General advice affects individuals poorly

> need customized lifestyle solutions

Social mediaapplication

Themes

mental wellbeing,stress & relaxation

loneliness & social wellbeing

physical fitness

nutrition and food

sleep

work and life

Question sets

”Appreciative inquiry”

Explorativedata analysis:

paths of wellbeing

Ongoing work:

VirtualCoach project

PI: Krista Lagus

Wellbeing data collections and analysis

DoctorsIllness &diseaseresearch

Research on wellbeing and

lifestyles

Coaches,peers,social

networksOUR FOCUS

”classical example”

SOM of wellbeing factorsamong Finnish youth

(Honkela, Koskinen, Koskenniemi & Karvonen, 2000)

Sports Institute of Finland(Vierumäki) fitness data

>100,000 measurements in 20+ yearssmall subset with also mental workload & stress evaluation

(Vatanen, Heikkilä Honkela, Kettunen, Lagus &Pantzar, 2012)

males females

example: abdominals

all

40-50 yearsold

What kind of different ”fitness groups” can be found?

Relationship between physical & mental wellbeing (stress)?

Do interventions help?

Sports Institute of Finland(Vierumäki) fitness data

>100,000 measurements in 20+ yearssmall subset with also mental workload & stress evaluation

(Vatanen, Heikkilä Honkela, Kettunen, Lagus &Pantzar, 2012)

males females

example: abdominals

all

40-50 yearsold

What kind of different ”fitness groups” can be found?

Relationship between physical & mental wellbeing (stress)?

Do interventions help?

Map of fitness and stress

Individual wellbeing paths onthe map of fitness and stress

Methodological view: We need...

● Big data on everyday life● Quantative measurements● Qualitative personal experiences

● Methods for● Dimensionality reduction● Information visualization● Time-series modeling● Text mining● Etc.

Identifying anomalous social contexts from mobile proximity data

using binomial mixture models

Eric Malmi, Juha Raitio, Oskar Kohonen, Krista Lagus, and Timo Honkela

IDA 2012

● Bluetooth data as an indicator of the social context

● The data tells about the people and devices nearby

● Period of time: 17 monts

● Data on 106 people, at least 90 days each

Text mining for wellbeing: Selecting stories using

semantic and pragmatic features

Timo Honkela, Zaur Izzatdust, Krista Lagus

ICANN 2012

Text mining for peer support

TOPIC ANALYSIS SENTIMENT ANALYSIS

Discussion forum postings, etc.

Selected stories

STYLEANALYSIS

MULTICRITERIA SELECTION PROCESS

User modelingand analysis of

feedback

EVALUATION

User'sinput

(Hon

kela

, Iz

zatd

ust,

Lag

us 2

012)

ICA of wellbeing-related termsin Reddit texts

(Honkela, Izzatdust, Lagus 2012)

Subjects on objects in contexts: Using GICA method to quantify

epistemological subjectivity

Timo Honkela, Juha Raitio, Krista Lagus, Ilari T. Nieminen, Nina Honkela, and Mika Pantzar

IJCNN 2012

Subjectifying: adding subjective views into object-context matrices

Outcome: Subject-Object-Context (SOC) Tensors

Potential sources for subjectification

● Conceptual surveys: ● individual assessment of contextual

appropriateness

● Text mining:● statistics of word/phrase-context patterns

● Empirical psychology:● reaction times, etc.

● Brain research

Flattening: unfolding 3-way tensorfor traditional 2-way analysis

OBJECTS:

Relaxation

Happiness

Fitness

Wellbeing

CONTEXTS:

SUBJECTS: Event participants

Case 1: Wellbeing concepts

MDS: Objects x Subjects

Fitness

NeRV: Objects x Subjects

Fitness

J. Venna, J. Peltonen, K. Nybo, H. Aidos, and S. Kaski. Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization. Journal of Machine Learning Research, 11:451-490, 2010.

NeRV:

SOM: Objects x Subjects

Case 2: State of the Union Addresses

● In this case, text mining is used for populating the Subject-Object-Context tensor

● This took place by calculating the frequencies on how often a subject uses an object word in the context of a context word● Context window of 30 words

Analysis of the word 'health'

Interactive SOMs:“Parametric modeling,

non-parametric visualization”

Timo Honkela and Michael Knapek

Unpublished, ongoing work

Interactive SOMs:“Making the analysis process and

variable selection more transparent”

Timo Honkela and Michael Knapek

Unpublished, ongoing work

ALTERNATIV

E TIT

LE

Data points “chase” BMUs

Thank you!Merci!Kiitos!

¡Gracias!

Obrigado!

Danke schön! ありがとう