Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures

Abstract

Personalizing wearable robots by incorporating user physiological feedback can improve energy efficiency and comfort. However, many current personalization methods are specific to a particular device and often require reprogramming, making them less accessible. In this study, we present an open-source, device-independent personalization framework that allows for human-in-the-loop optimization. This modular framework includes cost functions and optimization algorithms that use a physiological response to optimize wearable robot parameters. We tested this framework in three case studies involving diverse subjects and wearable robots. The first case study focused on personalizing an ankle-foot prosthesis using indirect calorimetry feedback. This resulted in a 5.3% and 18.1% reduction in metabolic cost for walking for two individuals with transtibial amputation, compared to the weight-based assistance. The second case study personalized a robotic ankle exoskeleton for three different walking speeds using indirect calorimetry feedback for two subjects. The metabolic cost was reduced by 1%, 2%, and 5.8% for one subject and by 20.8%, 1.9%, and 19% for the other subject, compared to a generic assistance condition for increasing speeds. The third case study personalized gait parameters, specifically step frequency, using an electrocardiogram (ECG)-based cost function along with an optimization algorithm variant, resulting in a 43% reduction in optimization time for one non-disabled subject. These case studies suggest that our personalization framework can effectively personalize wearable robot parameters and potentially enhance assistance benefits.