Paths of Wellbeing on Self-Organizing Maps + excerpts from other presentations
-
Upload
timo-honkela -
Category
Technology
-
view
480 -
download
0
description
Transcript of Paths of Wellbeing on Self-Organizing Maps + excerpts from other presentations
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