Existing automated fall detection devices are lacking in their ability to detect falls among wheelchair users. A new TechSAge article in Assistive Technology highlights the development of a fall detection algorithm, developed in a laboratory setting using machine learning techniques, that can accurately differentiate between wheelchair-related falls and wheelchair mobility activities. Researchers conducted a pilot study wherein 30 young, healthy, and ambulatory adults simulated 258 wheelchair falls and 220 wheelchair mobility activities in a lab with fall data retrieved from accelerometers worn on participants’ wrist, chest, and head. Findings indicate that the algorithm should be integrated into wrist-worn devices and further tested among wheelchair users to evaluate its ability to minimize consequences from falls. Request the full article by Libak Abou, Alexander Fliflet, Peter Presti, Jacob Sosnoff, Harshal Mahajan, Mikaela Frechette, and Laura Rice.

Ivy Rubio-Ramirez is a senior Architecture student at Georgia Tech and a work-study research assistant at the Center for Inclusive Design and Innovation. Ivy supports the TechSAge team with a variety of clerical tasks, including conducting literature reviews, researching population statistics, automating references in EndNote, and supporting student design competitions. Additionally, Ivy interns at AKA Studio P.C., a local architecture firm. In her free time, Ivy enjoys caring for her plants and binge-watching movies through her movie theater membership.