Velocity modulation assistance for stroke rehabilitation based on EMG muscular condition

Robotic-assisted systems have been playing a key role in improving and speeding up motor recovery during stroke rehabilitation therapies. This paper presents an approach to determine velocity patterns based on the analysis of the EMG muscular condition of the hand. To this purpose, we conducted an experimental protocol with 18 subjects participating as volunteers, with the aim of acquiring EMG signals for three levels of the muscular condition: non-fatigue, transition-to-fatigue, and fatigue. Artificial Neural Networks (ANN) were trained to identify the aforementioned muscular condition levels, while a Sugeno-Type Fuzzy Inference system was used to determine the velocity based on the output of the ANN classifiers. Results indicate the proposed approach can be used for the accurate modulation of pinch-grip therapies according to the muscular condition. These are promising results towards the development of EMG-driven robotic-assistance rehabilitation therapies for stroke patients.