Abstract
Current kinematic analysis for patients with upper or lower extremity challenges is usually performed indoors at the clin- ics, which may not always be accessible for all patients. On the other hand, mobility scooter is a popular assistive tool used by people with mobility disabilities. In this study, we introduce a remote kinematic analysis system for mobility scooter riders to use in their local communities. In order to train the human pose estimation model for the kinematic anal- ysis application, we have collected our own mobility scooter riding video dataset which captures riders’ upper-body move- ments. The ground truth data is labeled by the collaborating clinicians. The evaluation results show high system accuracy both in the keypoints prediction and in the downstream kine- matic analysis, compared with the general-purpose pose mod- els. Our efficiency test results on NVIDIA Jetson Orin Nano also validate the feasibility of running the system in real-time on edge devices.
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