Uncertainty-Aware Ankle Exoskeleton Control

Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, restricting their real-world applicability. We present an uncertainty-aware control framework that enables ankle exoskeletons to operate safely across diverse scenarios by automatically disengaging when encountering unfamiliar movements. Our approach uses an uncertainty estimator to classify movements as similar (in-distribution) or different (out-of-distribution) relative to actions in the training set. We evaluated three architectures (model ensembles, autoencoders, and generative adversarial networks) on an offline dataset and tested the strongest performing architecture (ensemble of gait phase estimators) online. The online test demonstrated the ability of our uncertainty estimator to turn assistance on and off as the user transitioned between in-distribution and out-of-distribution tasks (F1: 89.2). This new framework provides a path for exoskeletons to safely and autonomously support human movement in unstructured, everyday environments.

Multiple Toddler Tracking in Indoor Videos

Multiple toddler tracking (MTT) involves identifying and differentiating toddlers in video footage. While conventional multi-object tracking (MOT) algorithms are adept at tracking diverse objects, toddlers pose unique challenges due to their unpredictable movements, various poses, and similar appearance. Tracking toddlers in indoor environments introduces additional complexities such as occlusions and limited fields of view. In this paper, we address the challenges of MTT and propose MTTSort, a customized method built upon the DeepSort algorithm. MTTSort is designed to track multiple toddlers in indoor videos accurately. Our contributions include discussing the primary challenges in MTT, introducing a genetic algorithm to optimize hyperparameters, proposing an accurate tracking algorithm, and curating the MTTrack dataset using unbiased AI co-labeling techniques. We quantitatively compare MTTSort to state-of-the-art MOT methods on MTTrack, DanceTrack, and MOT15 datasets. In our evaluation, the proposed method outperformed other MOT methods, achieving 0.98, 0.68, and 0.98 in multiple object tracking accuracy (MOTA), higher order tracking accuracy (HOTA), and iterative and discriminative framework 1 (IDF1) metrics, respectively.