An EMG-Based Biofeedback System for Tailored Interventions Involving Distributed Muscles

Electromyography (EMG)-based biofeedback has considerable potential as a mode of therapeutic exercise for individuals with motor impairments. Advances in technology now allow the delivery of biofeedback with cheap, compact computers, and sensors. Reliable machine learning classification of EMG signals in real time, which is a core component of many biofeedback systems, is also now easily accessible through various open-source software libraries. Despite this progress and the attention garnered by EMG biofeedback among researchers, broad clinical acceptance remains elusive. We aim to open this technology to a broader audience by proposing an accessible standard approach to the design and implementation of EMG-based biofeedback systems. We highlight important considerations when designing a system to deliver potent biofeedback, including maximizing motivation, minimizing constraints on sensor number or configuration, and maximizing replicability by other researchers. Based on relevant neuroscientific and technical literature, we recommend methods and procedures by which these goals can be achieved. Finally, we create and test a biofeedback system in a sample of both healthy and motor impaired volunteers. We found that the EMG biofeedback system supported accurate and stable control by healthy and impaired users, and could be implemented with minimal access to coding expertise and an off-the-shelf EMG device. This work expands awareness of effective design principles for EMG biofeedback systems and will advance the state-of-the-art in this field.