A Low-Cost Telerehabilitation Paradigm for Bimanual Training

The COVID-19 pandemic has transformed daily life, as individuals must reduce contacts among each other to prevent the spread of the disease. Consequently, patients' access to outpatient rehabilitation care was curtailed and their prospect for recovery has been compromised. Telerehabilitation has the potential to provide these patients with equally efficacious therapy in their homes. Using commercial gaming devices with embedded motion sensors, data on movement can be collected toward objective assessment of motor performance, followed by training and documentation of progress. Herein, we present a low-cost telerehabilitation system dedicated to bimanual exercise, wherein the healthy arm drives movements of the affected arm. In the proposed setting, a patient manipulates a dowel embedded with a sensor in front of a Microsoft Kinect sensor. In order to provide an engaging environment for the exercise, the dowel is interfaced with a personal computer, to serve as a controller. The patient's gestures are translated into actions in a custom-made citizen-science project. Along with the system, we introduce an algorithm for classification of the bimanual movements, whose inner workings are detailed in terms of the procedures performed for dimensionality reduction, feature extraction, and movement classification. We demonstrate the feasibility of our system on eight healthy subjects, offering support to the validity of the algorithm. These preliminary findings set forth the development of precise motion analysis algorithms in affordable home-based rehabilitation.