Radar-based Real-Time Detection of Overdose Incidents in Small Public Spaces

This project aims to train machine learning models that use millimetre-wave radar data to non-invasively detect health incidents, such as overdoses in public bathroom stalls. The partner, Brave Technology Coop, has developed a small, wall-mounted, radar sensing unit on which we want to directly deploy real-time gesture detection software, an instance of cutting-edge “Edge AI.” The unit can then immediately notify a responder if it detects a target incident, which otherwise would not be detected until a passerby notices.

The project will improve Brave’s ability to accurately classify these incidents, thereby strengthening their ODetect sensor product. Additionally, it will investigate other health-related gestures that may diversify the product’s target market.

Faculty Supervisor:

Sean Chester

Student:

Benjamin Smith;Parisa Esmaeilian Ghahroudi

Partner:

Brave Technology Coop

Discipline:

Computer science

Sector:

Other

University:

University of Victoria