Background The rhythm of music can entrain neurons in motor cortex by way of direct connections between auditory and motor brain regions. Objective We sought to automate an individualized and progressive music-based, walking rehabilitation program using real-time sensor data in combination with decision algorithms. Methods A music-based digital therapeutic was developed to maintain high sound quality while modulating, in real-time, the tempo (ie, beats per minute, or bpm) of music based on a user’s ability to entrain to the tempo and progress to faster walking cadences in-sync with the progression of the tempo. Eleven individuals with chronic hemiparesis completed one automated 30-minute training visit. Seven returned for 2 additional visits. Safety, feasibility, and rehabilitative potential (ie, changes in walking speed relative to clinically meaningful change scores) were evaluated. Results A single, fully automated training visit resulted in increased usual (∆ 0.085 ± 0.027 m/s, P = .011) and fast (∆ 0.093 ± 0.032 m/s, P = .016) walking speeds. The 7 participants who completed additional training visits increased their usual walking speed by 0.12 ± 0.03 m/s after only 3 days of training. Changes in walking speed were highly related to changes in walking cadence ( R2 > 0.70). No trips or falls were noted during training, all users reported that the device helped them walk faster, and 70% indicated that they would use it most or all of the time at home. Conclusions In this proof-of-concept study, we show that a sensor-automated, progressive, and individualized rhythmic locomotor training program can be implemented safely and effectively to train walking speed after stroke. Music-based digital therapeutics have the potential to facilitate salient, community-based rehabilitation.

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Biomarkers that can identify age-related decline in walking function have potential to promote healthier aging by triggering timely interventions that can mitigate or reverse impairments. Recent evidence suggests that changes in neuromuscular control precede changes in walking function; however, it is unclear which measures are best suited for identifying age-related changes. In this study, non-negative matrix factorization of electromyography data collected during treadmill walking was used to calculate two measures of the complexity of muscle co-activations during walking for 36 adults: (1) the number of muscle synergies and (2) the dynamic motor control index. Study participants were grouped into young (18–35 years old), young-old (65–74 years old), and old–old (75+ years old) subsets. We found that the dynamic motor control index [χ2(2) = 9.41, p = 0.009], and not the number of muscle synergies [χ2(2) = 5.42, p = 0.067], differentiates between age groups [χ2(4) = 10.62, p = 0.031, Nagelkerke R2 = 0.297]. Moreover, an impairment threshold set at a dynamic motor control index of 90 (i.e., one standard deviation below the young adults) was able to differentiate between age groups [χ2(2) = 9.351, p = 0.009]. The dynamic motor control index identifies age-related differences in neuromuscular complexity not measured by the number of muscle synergies and may have clinical utility as a marker of neuromotor impairment.

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.

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