Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke.

Octavio Marin-Pardo,Sook-Lei Liew,Coralie Phanord,Christopher M Laine,Miranda Rennie Donnelly

doi:10.3390/s21051806

Octavio Marin-Pardo, Sook-Lei Liew + Show 3 more

Open Access

https://doi.org/10.3390/s21051806

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Abstract

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.

Highlights

Stroke is a leading cause of long-term disability in the United States with almost800,000 people experiencing a new or recurrent stroke each year [1]
We report the development of a muscle-computer interface to train muscle activity from wrist extensors while limiting unintended coactivation of wrist flexors for at-home chronic stroke telerehabilitation
We provided a validation example in two healthy individuals, comparing the use of low-cost sensors to calculate ratios of muscle activity versus research-grade sensors while performing the same task

Summary

Introduction

Stroke is a leading cause of long-term disability in the United States with almost800,000 people experiencing a new or recurrent stroke each year [1]. While motor recovery was thought to plateau by the chronic stage after stroke (more than 6 months after the vascular incident), more recent studies have shown that improvement of upper limb function is possible at the chronic stage [2,3]. Recent research suggests that high dose interventions of repeated task-specific practice are effective at inducing significant positive outcomes in this population [3,4,5,6]. Due to the time and physical constraints of many therapy sessions, common in-clinic interventions only provide on average 32 repetitions of functional upper extremity movements per session [7]. Recent studies suggest that telerehabilitation for stroke rehabilitation is feasible and as effective as in-person therapy [8,9]

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