A New Visual Biofeedback Protocol Based on Analyzing the Muscle Synergy Patterns to Recover the Upper Limbs Movement in Ischemic Stroke Patients: A Pilot Study

Upper limb functional disability is a common after-effect among stroke survivors. The main goal of this study was to present a visual biofeedback protocol to identify a model based on synergy patterns of the elbow muscles for motor learning and rehabilitation of stroke survivors with hemiparesis.

First, kinematic data related to the position of four joints and the surface electromyography signal of four muscles involved in the arm movement in the transverse plane were collected, preprocessed, and synchronized. In the next step, muscle synergy patterns were extracted using the Hierarchical Alternating Least Squares (HALS) method, and at the same time, kinematic data were recorded by the modified MediaPipe algorithm. Finally, a deep learning model based on the Gated Recursive Unit (GRU) was used to map between them. The model output was regarded as the visual biofeedback trajectory to conduct the exercise therapy by the patients.

The evaluations showed that the path produced by the proposed model is potentially suitable for visual biofeedback. Moreover, the artificial neural network based on GRU architecture has had the best performance in generating the visual biofeedback trajectory.

Experimental and clinical evaluations will show that participants can acceptably follow the visual trajectory generated by the model. Therefore, this mechanism can be used to improve and develop biofeedback systems to accelerate the functional rehabilitation of patients with hemiplegia caused by ischemic stroke along with other conventional rehabilitation methods.