Autonomous Control of Music to Retrain Walking After Stroke

Abstract

Background Post-stroke care guidelines highlight continued rehabilitation as essential; however, many stroke survivors cannot participate in outpatient rehabilitation. Technological advances in wearable sensing, treatment algorithms, and care delivery interfaces have created new opportunities for high-efficacy rehabilitation interventions to be delivered autonomously in any setting (ie, clinic, community, or home). Methods We developed an autonomous rehabilitation system that combines the closed-loop control of music with real-time gait analysis to fully automate patient-tailored walking rehabilitation. Specifically, the mechanism-of-action of auditory-motor entrainment is applied to induce targeted changes in the post-stroke gait pattern by way of targeted changes in music. Using speed-controlled biomechanical and physiological assessments, we evaluate in 10 individuals with chronic post-stroke hemiparesis the effects of a fully-automated gait training session on gait asymmetry and the energetic cost of walking. Results Post-treatment reductions in step time (Δ: −12 ± 26%, P = .027), stance time (Δ: −22 ± 10%, P = .004), and swing time (Δ: −15 ± 10%, P = .006) asymmetries were observed together with a 9 ± 5% reduction (P = .027) in the energetic cost of walking. Changes in the energetic cost of walking were highly dependent on the degree of baseline energetic impairment (r =− .90, P < .001). Among the 7 individuals with a baseline energetic cost of walking larger than the normative value of healthy older adults, a 13 ± 4% reduction was observed after training. Conclusions The closed-loop control of music can fully automate walking rehabilitation that markedly improves walking after stroke. Autonomous rehabilitation delivery systems that can safely provide high-efficacy rehabilitation in any setting have the potential to alleviate access-related care gaps and improve long-term outcomes after stroke.