In this presentation in WSOM 2012 conference, we introduce the concept of pathways of wellbeing
and examine how such paths can be discovered from large data
sets using the self-organizing map. Data sets used in the illustrative experiments
include measurements of physical fitness and subjective assessments
related to diagnosing work stress. In addition, we show results from related projects.
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Paths of Wellbeing on Self-Organizing Maps + excerpts from other presentations
1. Paths of Wellbeing on
Self-Organizing Maps
Krista Lagus Aalto University
(former Helsinki University of Technology)
Tommi Vatanen Sports Institute of Finland
Oili Kettunen
Stressinmurtajat
Antti Heikkilä
National Consumer Research Center
Matti Heikkilä
Mika Pantzar
Finland
Timo Honkela
2. 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
3. Ongoing work:
VirtualCoach project
PI: Krista Lagus
Question sets Themes
”Appreciative
inquiry” mental wellbeing,
stress & relaxation
loneliness & social
Social media wellbeing
application Explorative physical fitness
data analysis: nutrition and food
sleep
paths of
work and life
wellbeing
4. Wellbeing data collections
and analysis
Illness &
disease Doctors
research
Coaches,
Research on
peers,
wellbeing and
social
lifestyles
OUR networks
FOCUS
5. ”classical example”
SOM of wellbeing factors
among Finnish youth
(Honkela, Koskinen, Koskenniemi & Karvonen, 2000)
6. Sports Institute of Finland
(Vierumäki) fitness data
>100,000 measurements in 20+ years
small subset with also mental workload & stress evaluation
example: abdominals
What kind of males females
different ”fitness all
groups” can be
found?
Relationship
between
physical & mental
40-50
wellbeing (stress)? years
old
Do interventions
help?
(Vatanen, Heikkilä Honkela, Kettunen, Lagus &Pantzar, 2012)
7. Sports Institute of Finland
(Vierumäki) fitness data
>100,000 measurements in 20+ years
small subset with also mental workload & stress evaluation
example: abdominals
What kind of males females
different ”fitness all
groups” can be
found?
Relationship
between
physical & mental
40-50
wellbeing (stress)? years
old
Do interventions
help?
(Vatanen, Heikkilä Honkela, Kettunen, Lagus &Pantzar, 2012)
10. 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.
11. 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
12. ● 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
13. Text mining for wellbeing:
Selecting stories using
semantic and pragmatic features
Timo Honkela, Zaur Izzatdust, Krista Lagus
ICANN 2012
14. Text mining for peer support
User's User modeling
Discussion forum input and analysis of
postings, etc. feedback
(Honkela, Izzatdust, Lagus 2012)
STYLE
TOPIC ANALYSIS SENTIMENT ANALYSIS
ANALYSIS
MULTICRITERIA SELECTION PROCESS
Selected stories
EVALUATION
16. 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
22. NeRV: Objects x Subjects
Fitness
NeRV:
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.
24. 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
26. Interactive SOMs:
“Parametric modeling,
non-parametric visualization”
Timo Honkela and Michael Knapek
Unpublished, ongoing work
27. E
TL
TI
IV
E
Interactive SOMs:
AT
“Making the analysis process and
N
ER
LT
variable selection more transparent”
A
Timo Honkela and Michael Knapek
Unpublished, ongoing work