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.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Gao, Yufei
• Flannery, Maurice Anthony
• Assan, Jerry Xu
• Wu, Yuxin
• Resom, Adonay Zenebe

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-032219-034328

Advisor

• Rundensteiner, Elke A.

Year

• 2019

Date created

• 2019-03-22

Resource type

• Major Qualifying Project

Major

• Computer Science

Rights statement

• In Copyright