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.

Sutanuka Bhattacharjya is an Assistant Professor in the Department of Occupational Therapy at Georgia State University. As part of TechSAge, Sutanuka supports the SmartBathroom project by assessing activity performance and feasibility of using technology in measurement of movement. Additionally, Sutanuka works on the TechSAge funded pilot app project DigiHand, which tests the feasibility of using technology in measuring the quality of upper limb movement during daily activities. Sutanuka’s research interests focus on assessing the use of technology to support individuals with chronic deficits in continuing their rehabilitation within their homes. Sutanuka is also professionally trained in Bharatnatyam, an Indian classical dance form.