Description:
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.
Background/History
A large number of subjects have signed up for this study to download and run a smartphone 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.
Purpose
This project aims to make use of our smart devices to capture essential health-related data, analyze it, and provide the community with open-source tools for mental health diagnostics in specific, and human health in general.
Benefits
This project will set standards in smartphone sensor big data processing and analysis, and it will provide analytical tools for the correlation of human activity with mood and mental health.
In this era where all mankind is suffering from stress, anxiety, and mental instability, the introduction of such tools and analyses for the improvement of mental health is of great value to human beings in general, and more particularly for patients suffering from mental illnesses and their medical caregivers.
Once developed and implemented, the tools and analytical pipelines generated by this project may be embedded on edge-devices (e.g. smartphones, smart watches, microcontroller-based systems) for online mental health assessment, or linked to the healthcare facility's network for advanced EHR, or even on the cloud so that medical providers can monitor and diagnose treatment effectiveness over the web.
How
This project will be carried out as a series of processes: ETL → EDA → QA/QC → ML/AI model engineering → MLOps pipeline integration
To achieve the desired outcomes, advanced unsupervised analysis and anomaly detection algorithms will be implemented for QA/QC, machine learning (shallow and deep) models will be built, tested and tuned for optimal performance, and operation pipelines will be developed for system automation and integration in the infrastructure.
Further reading
Any papers or article that I can use to understand better
Additional information
Anything that you think is relevant that doesn’t fit under any of the sections above.
https://healthstudy.tozny.com/help
| Resource | Location |
|---|---|
| Point of Contact | Unknown User (abhishek_pratap) Ramzi HalabiUnknown User (calvin_herd) (sensor data) Unknown User (sophia_li)(survey data) |
| Admin | |
| Contractor | |
| 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) |
| Other Public Websites | https://psychiatry.uw.edu/project/hippocratic-app-study/ |
| Enrollment Website | https://healthstudy.tozny.com/help |