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Current kinematic analysis for patients with upper or lowerextremity challenges is usually performed indoors at the clin-ics, which may not always be accessible for all patients. Onthe other hand, mobility scooter is a popular assistive toolused by people with mobility disabilities. In this study, weintroduce a remote kinematic analysis system for mobilityscooter riders to use in their local communities. In order totrain the human pose estimation model for the kinematic anal-ysis application, we have collected our own mobility scooterriding video dataset which captures riders’ upper-body move-ments. The ground truth data is labeled by the collaboratingclinicians. The evaluation results show high system accuracyboth 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 Nanoalso validate the feasibility of running the system in real-timeon edge devices.
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Rider Posture-Based Continuous Authentication with Few-Shot Learning for Mobility Scooters (Student Abstract)
Current practice of mobility scooter user authentication using physical keys and traditional password-based one-time security mechanisms cannot meet the needs of many mobility scooter riders, especially senior citizens having issues in recalling memory. Now seamless authentication approaches are needed to provide ongoing protection for mobility scooters against takeovers and unauthorized access. Existing continuous authentication techniques do not work well in a mobility scooter setting due to issues such as user comfort, deployment cost and enrollment time, among others. In that direction, our contributions in this research effort are two-fold: (i) we propose a novel system that incorporates advances in few-shot learning, hierarchical processing, and contextual embedding to establish continuous authentication for mobility scooter riders using only posture data. This security system, trained on data collected from real mobility scooter riders, demonstrates quick enrollment and easy deployability, while successfully serving as an unobtrusive first layer of security. (ii) we provide to the research community the largest publicly available repository of mobility scooter riders' body key-points data to enable further research in this direction.
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- PAR ID:
- 10505526
- Publisher / Repository:
- AAAI Press
- Date Published:
- Journal Name:
- Proceedings of the AAAI Conference on Artificial Intelligence
- Volume:
- 38
- Issue:
- 21
- ISSN:
- 2159-5399
- Page Range / eLocation ID:
- 23647 to 23648
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aaai.v38i21.30509
