Paths of Wellbeing on Self-Organizing Maps + excerpts from other presentations
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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)
- 8. Map of fitness and stress
- 9. Individual wellbeing paths on the map of fitness and stress
- 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
- 15. ICA of wellbeing-related terms in Reddit texts (Honkela, Izzatdust, Lagus 2012)
- 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
- 17. Subjectifying: adding subjective views into object-context matrices Outcome: Subject-Object-Context (SOC) Tensors
- 18. 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
- 19. Flattening: unfolding 3-way tensor for traditional 2-way analysis
- 20. Case 1: Wellbeing concepts CONTEXTS: OBJECTS: Relaxation Happiness Fitness Wellbeing SUBJECTS: Event participants
- 21. MDS: Objects x Subjects Fitness
- 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.
- 23. SOM: Objects x Subjects
- 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
- 25. Analysis of the word 'health'
- 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
- 28. Data points “chase” BMUs
- 29. Thank you! ¡Gracias! Kiitos! Merci! Obrigado! Danke schön! ありがとう