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4.2 -  BeWell+: Multi-dimensional Wellbeing Monitoring with Community-guided User Feedback and Energy Optimization
 

4.2 - BeWell+: Multi-dimensional Wellbeing Monitoring with Community-guided User Feedback and Energy Optimization

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Wednesday, October 24, 2012 ...

Wednesday, October 24, 2012
Technical Session #4

Mu Lin (Dartmouth College, US), Nicholas Lane (Microsoft Research Asia, CN), Mashfiqui Mohammod (Cornell University, US), Xiaochao Yang (Dartmouth College, US), Hong Lu (Dartmouth College, US), Giuseppe Cardone (University of Bologna, IT), Shahid Ali (Dartmouth College, US), Afsaneh Doryab (IT University of Copenhagen, DK), Ethan Berke (Dartmouth College, US), Andrew Campbell (Dartmouth College, US), Tanzeem Choudhury (Cornell University, US)


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  • Before the presentation, I would like to show you a youtube video which introduces the motivation, basic idea of this project, and gives an overview of the actual bewell app. You get the basic idea of bewell. And we officially released bewell on google early this year. So today I will primarily talk about the technique details of our application.
  • As mentioned by the video. bewell app monitors people’s wellbeing states in three dimensions:Here is the overall design of bewell+.From top-down, we have three main steps in our pipeline
  • To break it down into more details, this is our behavior monitoring component. For each dimension, we use the distinctive sensor and extract aset of informative features from the sensor reading.
  • After the system extracts user’s behavior patterns, it converts them into wellbeing scores via certain medical metrics.e.g. 1-hour running and 3-hour walking will give a score say 85.Then these scores are sent to the live wallpaper to drive its dynamic visualization, as you already saw in the video.
  • Currently we only have one single ideal expectations to all users. People usually have really different life styles and working schedules. In our current model,And each individual feedback should also only consider the states of those similar users.
  • Find people with similar life styles, group them together and only provide feedback based on their relative performance. They will have more motivation to improve their wellbeing.
  • This is an extreme example. The general idea is thatthere are certain underlying communities existing even in high diverse population in which people share similar life patterns. Will people in these groups find more stimulated and confident to improve their life.
  • Tofigure this out, we designed a new scoring approach. This approach adapts generic wellbeing score functions based on behavior similarity within user population. Here is the architectureBy only taking into account the performance of their peers people.
  • We tested the community adaptive approach on the data collected from our field deployment. Here are some results from an example user in physical dimension for 7 days. The x-axis is…, y-axis is… By adapting wellbeing score functions users receive feedback that more accurately reflect their relative position with people who have similar lifestyles. And we believe that this adaptive feedback can better motivate people to maintain and improve their everyday life. For other two dimensions, the situation is similar.
  • In the end of this talk

4.2 -  BeWell+: Multi-dimensional Wellbeing Monitoring with Community-guided User Feedback and Energy Optimization 4.2 - BeWell+: Multi-dimensional Wellbeing Monitoring with Community-guided User Feedback and Energy Optimization Presentation Transcript

  • BeWell+ Multi-dimensional Wellbeing Monitoring APP with Community-guided User FeedbackMu Lin, Nicholas D. Lane, Mashfiqui Mohammod, Xiaochao Yang,Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, Ethan Berke, Andrew T. Campbell, Tanzeem ChoudhuryDartmouth College, Cornell University, Microsoft Research Asia, University of Bologna, University of Copenhagen
  • BeWell+ App Design Monitor Behavior Behavior Patterns Monitor Wellbeing Wellbeing Scores Inform & Promote Wellbeing User Feedback Physical Sleep Social
  • microphone, accelerometer,Behavior Patterns phone state1. Physical Activity GMM time and frequency based features{walk, run, stationary} (e.g., spectral-roll off, zero- duration crossing, mean, variance)2. Social Interaction Decision Tree {voicing, non- time and frequency voicing} based features (e.g., MFCC, zero-crossing, duration mean, variance)3. Sleep Patterns Linear Regression sleep Duration of phone events duration based features (e.g., charging, dark, stationa ry and silence events)
  • Behavior Patterns User FeedbackWellbeing Scores1. Physical Activity {Linear Regression} Physical score2. Social Interaction {Linear Regression} Social score3. Sleep Patterns {Gaussian Function} Sleep score
  • Preliminary System Experiments• Dartmouth College Students/Faculty/Staff• 27 people field study• 19 effective days• Multi-dimensional group V.S. control group
  • BeWell+ promotes positive changes in people’s behavior Subjects with feedback generally have larger score increases
  • However…User diversity: one single ideal “healthy” expectationsmay not be consistently realistic for everyone
  • However…User diversity: one single ideal “healthy” expectationsmay not be consistently realistic for everyone
  • • User community exists• Provide more realistic feedback and set up more feasible goals to each community
  • Community Adaptive Wellbeing Scoring
  • Example of Community-Guided Feedback
  • BeWell+ APP Check it out on Google Play!
  • Thank You!