Turn Detection and Characterization with Inertial Sensors

Turn detection and characterization in the home is important for continuous assessment of gait and balance in people with movement disability. Turning often results in falling in individuals with movement disorders. Researchers and clinicians would benefit from a system that identifies and characterizes their daily mobility behavior to predict their risk of falling, benefits or side effects of treatment, and progression of disease. The goal of this study is to develop an algorithm that is capable of reliably detecting turns during gait with the goal of applying it over long periods outside a lab environment. Performance of the algorithm is validated against an optical marker system and video analysis of a subset of the participants.