Rider Posture-Based Continuous Authentication with Few-Shot Learning for Mobility Scooters (Student Abstract)


  • Devan Shah Princeton University
  • Ruoqi Huang California State Polytechnic University, Pomona
  • Tingting Chen California State Polytechnic University, Pomona
  • Murtuza Jadliwala University of Texas, San Antonio




Applications Of AI, Continuous Authentication, Pose Estimation, Few Shot Learning


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.



How to Cite

Shah, D., Huang, R., Chen, T., & Jadliwala, M. (2024). Rider Posture-Based Continuous Authentication with Few-Shot Learning for Mobility Scooters (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23647-23648. https://doi.org/10.1609/aaai.v38i21.30509