Developing an Information System for Assistive Technology Apps

The goal of this project was to create an easy, accessible way for Seven Hills employees to find apps to assist the various needs of their clients. To accomplish this goal, we have created the following deliverables: an assistive technology (AT) apps database, and both written and video tutorials for the database. This system currently contains over four hundred apps that are searchable by various identifiers such as name, category, and disability to name a few. It also allows users to request new apps to be added, and allows administrators to edit and delete apps. Most of the research the staff conducts are through peer review, so the system also includes rankings and comments. The tutorials are for both users and administrators, and explain how to search, edit, and maintain the database

Machine Learning for Mental Health Detection

Our project goal was to develop a depression sensing application that leverages multi-modal data sources collected from a smartphone, focusing on features extracted from audio, text messages, social media data, as well as GPS modalities. We conducted extensive experiments to study the effectiveness of these features to improve our machine learning model. We deployed our EMU app on Amazon Mechanical Turk for crowd-sourced data collection and incorporated feature extraction techniques and machine learning algorithms to reliably predict levels of depression