Basic evaluation of limb exercises based on electromyography and classification methods

Symptoms caused by cerebral palsy or stroke deprive a person partially or even completely of his ability to move. Nowadays we can observe more technologically advanced rehabilitation devices which incorporate biofeedback into the process of rehabilitation of such people. However, there is still a lack of devices that would analyse, assess, and control (independently or with limited support) specialised movement exercises. Here we propose an idea of an automated exercise evaluation mechanism based on machine learning techniques, such as: support vector machines, decision trees, random forest, and k-nearest neighbours. While being only a preliminary case study, our research showed that with appropriate processing even a 100% accuracy score can be achieved in classifying whether an exercise is executed well or not.