Characterization of an Algorithm for Autonomous, Closed-Loop Neuromodulation During Motor Rehabilitation.

BACKGROUND: Recent evidence demonstrates that manually triggered vagus nerve stimulation (VNS) combined with rehabilitation leads to increased recovery of upper limb motor function after stroke. This approach is premised on studies demonstrating that the timing of stimulation relative to movements is a key determinant in the effectiveness of this approach. OBJECTIVE: The overall goal of the study was to identify an algorithm that could be used to automatically trigger VNS on the best movements during rehabilitative exercises while maintaining a desired interval between stimulations to reduce the burden of manual stimulation triggering. METHODS: To develop the algorithm, we analyzed movement data collected from patients with a history of neurological injury. We applied 3 different algorithms to the signal, analyzed their triggering choices, and then validated the best algorithm by comparing triggering choices to those selected by a therapist delivering VNS therapy. RESULTS: The dynamic algorithm triggered above the 95th percentile of maximum movement at a rate of 5.09 (interquartile range [IQR] = 0.74) triggers per minute. The periodic algorithm produces stimulation at set intervals but low movement selectivity (34.05%, IQR = 7.47), while the static threshold algorithm produces long interstimulus intervals (27.16 ± 2.01 seconds) with selectivity of 64.49% (IQR = 25.38). On average, the dynamic algorithm selects movements that are 54 ± 3% larger than therapist-selected movements. CONCLUSIONS: This study shows that a dynamic algorithm is an effective strategy to trigger VNS during the best movements at a reliable triggering rate.

Usage of RePlay as a Take-Home System to Support High-Repetition Motor Rehabilitation After Neurological Injury.

Stroke is a leading cause of chronic motor disability. While physical rehabilitation can promote functional recovery, several barriers prevent patients from receiving optimal rehabilitative care. Easy access to at-home rehabilitative tools could increase patients' ability to participate in rehabilitative exercises, which may lead to improved outcomes. Toward achieving this goal, we developed RePlay: a novel system that facilitates unsupervised rehabilitative exercises at home. RePlay leverages available consumer technology to provide a simple tool that allows users to perform common rehabilitative exercises in a gameplay environment. RePlay collects quantitative time series force and movement data from handheld devices, which provide therapists the ability to quantify gains and individualize rehabilitative regimens. RePlay was developed in C# using Visual Studio. In this feasibility study, we assessed whether participants with neurological injury are capable of using the RePlay system in both a supervised in-office setting and an unsupervised at-home setting, and we assessed their adherence to the unsupervised at-home rehabilitation assignment. All participants were assigned a set of 18 games and exercises to play each day. Participants produced on average 698 ± 36 discrete movements during the initial 1 hour in-office visit. A subset of participants who used the system at home produced 1593 ± 197 discrete movements per day. Participants demonstrated a high degree of engagement while using the system at home, typically completing nearly double the number of assigned exercises per day. These findings indicate that the open-source RePlay system may be a feasible tool to facilitate access to rehabilitative exercises and potentially improve overall patient outcomes.