Wearable Sensing and Haptic Feedback Research Platform for Gait Retraining

Junkai Xu, Unghee Lee1, Tian Bao, Yangjian Huang, Kathleen H. Sienko, Peter B. Shull

  • 1University of Michigan

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Contributed Papers (Oral)

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08:45 - 09:30 | Thu 11 May | Einstein Auditorium | ThAT1

Technical Session 5 – Objective Gait Assessment

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Abstract

Gait retraining is an important rehabilitation method for re-establishing health gait patterns resulting from disease or injury. Optical marker-based motion capture systems are effective for sensing but aren't used widely, due to cost and lack of portability. Moreover, to perform gait retraining, feedback is needed in addition to sensing. This paper presents a wearable sensing and haptic feedback research platform for gait retraining. The platform contains eight distributed nodes (Dots) and a central control unit (Hub) that wirelessly connects to the Dots. Each Dot provides 9-axis inertial sensing and can be configured for sensing or/and providing vibrotactile feedback according to movement training requirements. The Hub receives the sensor data, performs algorithm computation and distributes feedback commands based on the feedback strategy. A foot progression angle (FPA) gait retraining task was performed by six healthy older adults. Participants used the wearable system to learn toe-in gait (foot pointing more inward) and toe-out gait (foot pointing more outward) by adjusting their FPA based on haptic cues to fall within the no feedback zone, i.e. the desired range of acceptable FPAs. After gait retraining, FPA during toe-in gait (1.8±5.6 deg) was significantly higher than during baseline walking (-4.3±5.1 deg) (p

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