Mobility scooters serve as a powerful last-mile transportation tool for people with mobility challenges. Given the unique riding behavior and posture of mobility scooter riders, such user-specific mobility scooter ride data has tremendous potential towards the design of continuous user identification and authentication mechanisms. However, there have been no prior research efforts in the literature exploring this unique modality for the design of continuous user identification techniques. To address this gap, this paper proposesScooterID, the first framework which employs rider posture data collected from cameras on mobility scooters to continuously identify (and authenticate) users/riders. As part of this framework, a machine learning based model comprising of a spatio-temporal Graph Convolutional Network and a body-part-informed encoder is designed to effectively capture a user’s subtle upper-body movements during mobility scooter rides into discriminating embedding vectors. These embeddings can then be used to reliably and continuously identify and authenticate users/riders. Experiments with real-world mobility scooter ride data show thatScooterIDachieves high levels of authentication accuracy with few enrollment video samples.ScooterIDalso performs efficiently on resource-constrained devices (e.g., Raspberry Pis) and is robust against adversarial perturbations to authentication inputs.

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.

Mobility scooters are critical in facilitating social participation of people with mobility challenges and thus improving their life quality. However, the safety issues of driving mobility scooter are real concerns and may not be assessed in a timely fashion for many patients. In this paper we enable driving posture stability analysis at home or in the community setting for people with mobility challenges by using only video recordings of their driving. In particular, we design a system that extracts upper body keypoints’ 2D coordinates from video frames and builds an autoencoder model to perform stability analysis. We explore two architectures of the autoencoder, with Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN), respectively emphasizing the temporal and spatial relationships of upper body keypoints in driving movements. Evaluations using patients driving posture data collected have shown that both architectures have achieved over 0.99 Precision-Recall Area Under the Curve (AUC), and 0.8 ROC AUC, indicating excellent model accuracy levels.