Date post: | 04-Aug-2015 |
Category: |
Data & Analytics |
Upload: | maulik-kamdar |
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Integrating Wearables and User Interaction
Patterns to Monitor Mental Health
Maulik R. Kamdar, Michelle J. Wu, Zeshan Hussain
Introduction
Noninvasive Quantitative Continuous
DSM-5 guidelines ✔ ✖ ✖
EEG ✖ ✔ ✖
Video Monitoring ✔ ✖ ✔
Our goal ✔ ✔ ✔
Problem
Aims
● Develop a web application that aggregates smart watch data and patient insights
● Provide proof of concept through preliminary data collection
● Extract underlying patterns in the data and demonstrate a method for predicting mental health status
Brain Health Platform
http://54.200.211.229/BrainHealth/index.php
Brain Health Platform
Brain Health Platform
Methods: Data Collection- 12 anonymized participants (Ages: 19-37)- Gear S watch (10 am - 5 pm)
- Light - Heart Rate (BPM, Peak-to-peak) - Accelerometer (acceleration, rotation) - Pedometer (total distance, speed, calories, step count)
- Brain Health Web Application (10 am, 1 pm and 4 pm).- Textual, subjective Insights- Keyboard Interactions (key press time, interkey latency, speed,
number of errors, number of presses - back keys, enter, Ctrl+Z)- mouse interactions logged (move speed, drag speed, clicks)
Methods: Data Analysis- Features
- mean, standard deviation & dominant frequency for smart watch data types (heart rate, light, steps, speed, distance, acceleration, rotation)
- total distance, steps, walk steps, run steps- mean & standard deviation for the distribution of interkey latencies of
each bigram and press times of each unigram- number of spelling errors, undos, backspaces- average mouse move/drag velocity, total move/drag distance
- Modeling- one training example for each hour of data collected- cross validation to select a model, with 20% of data withheld for
evaluation
Conclusions & Future Work- We developed an integrated smart watch and web application that can
collect a variety of passive data for a user as well as provide feedback to the user through visualizations.
- Through a pilot study, we were able to demonstrate the efficient integration of many data types, with the potential for use as a predictor of emotional state.
- In the future, we hope to address data quality issues by developing smarter imputation and noise filtering methods.
- In addition, we plan to extend this study to patients with neuropsychiatric disorders and continue to develop our models for predicting patient status.
Acknowledgements
- Pilot study participants- Samsung- Dave Stark & Team mHealth- Diego Calderon- Reviewers- Russ Altman, Steve Bagley, Tim Sweeney
Thank you!
[email protected] · [email protected] · [email protected]
“Exogenous data (behavioral, social, environmental) is overwhelming, but 70% determinants of health occur in these data types” - Rob Merkel, IBM (Big Data in Biomedicine Conference 2015)