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.

Sujana Vangala is a 1st year Literature, Media, & Communications student at Georgia Tech. As the TechSAge digital communications and social media intern, Sujana helps increase awareness of the Center’s activities and promote engagement with community stakeholders. She manages TechSAge’s online presence, including our website, annual newsletter, Twitter, and LinkedIn. She is excited to create and disseminate content on behalf of the TechSAge team at the forefront of innovative technology development for people aging with disability. In her free time, Sujana can be found writing, going to concerts, or baking cookies with her family!