As an emerging neurostimulation for improving motor rehabilitation, applying vagus nerve stimulation (VNS) after successful movement during training facilitates motor recovery in animals with neuromotor impairment. To translate this procedure to human rehabilitation in a non-invasive, objective, and automated manner, real-time classification of movement quality on a trial-by-trial basis in a minimally constrained state is required. In this work, we developed an integrated closed-loop system using video-based real-time movement classification that can automatically trigger transcutaneous VNS (tVNS) wirelessly as soon as successful movement is detected. We also created a film-like conformable tVNS electrode to be attached over the outer ear. For movement training, we focused on the use case of dance therapy (backward walking), which is widely used for people with Parkinson’s disease and older adults. Our markerless video analysis model could detect steps with 0.91 precision and 0.72 recall and classify successful backward steps with a 0.93 F1 score. The classification triggers tVNS through Bluetooth Low Energy communications with a trigger relay device we created. The integrated system enabled real-time automated classification and stimulation, triggering tVNS with 71.3% of the successful movements and taking 2.24 s from video capture to tVNS. We consider our work to be an important step toward patient-driven rehabilitation at home showcasing non-invasive, low-cost, and automated closed-loop neurostimulation technologies.

The authors have declared no competing interest.

This work was supported in part by the McCamish Parkinson's Disease Innovation Program at Georgia Tech and Emory University.

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IRB of Georgia Institute of Technology gave ethical approval for this work

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