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| title | Description |
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This project is a smartphone sensor data analytics project that deals with a full set of heterogeneous datasets to be correlated with mood analysis and mental health. The acquired data includes periodically-prompted user survey entries, light sensor output, Bluetooth and WiFi status, GPS data, and multivariate IMU time series e.g. 3-axis accelerometer and 3-axis gyroscope output at simple and complex activity contexts.
| Resource | Location |
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| Point of Contact | Unknown User (abhishek_pratap) Ramzi HalabiUnknown User (calvin_herd) (sensor data) Unknown User (sophia_li)(survey data) |
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| Tech JIRA Project | |
| Code Repo | https://github.com/aid4mh/washdata_analysis (data analysis related) https://github.com/aid4mh/washstudy_ops (operational related) |
| Timeline | https://docs.google.com/spreadsheets/d/1-6ShkuSEzzxG1QlJQ7nX-KytWkFWNzGvuKQdymgqLkA/edit?usp=sharing (WASH Compliance Study 2022Q1 Timeline) https://docs.google.com/spreadsheets/d/1tnzbnyDnKVXcOucRQgaANO5uJfNzIdxvqHNMgTZEPLQ/edit?usp=sharing (WASH Sensor Data QA/QC 2022Q1 Timeline) |
| Other Public Websites | https://psychiatry.uw.edu/project/hippocratic-app-study/ |
| Enrollment Website | https://healthstudy.tozny.com/help |
| Study protocol and participant consent | Consent: https://drive.google.com/file/d/1t40rf0pEvcczgVlCkCbiics4ko2lNzS1/view?usp=sharing |
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The Warfighter Analytics using Smart Phones for Health (WASH) program recruited 25,000 participants across the United States to download and run a newly designed cell phone app named Health & Injury Prediction and Prevention Over Complex Reasoning and Analytic Techniques Integrated on a Cell Phone App (HIPPOCRATIC App) (Android and iOS) on their personal devices and allow specific and partially-prompted data acquisition that ranges from their answers to survey question to sensor data recording at various activity contexts. The effect of mood and mental on human activity, whether it is smartphone use activity or actual physical activity, has been an active research track for decades. Human activity modeling and recognition may be performed using wearable sensors and/or smartphone sensors, and our study deals with the latter. This research was inspired by the fact that almost everyone nowadays has a smartphone that they are constantly using, which makes accessing valuable data to analyze and improve people's health an easy-to-achieve task. With the multidisciplinary expertise in our team, we are developing and deploying state-of-the-art data analytics and machine learning pipelines to provide tools for better mental health diagnostics. |
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