A hybrid real-time EMG intelligent rehabilitation robot motions control based on Kalman Filter, support vector machines and particle swarm optimization

Intelligent Control of agent autonomous rehabilitation robot is a very complex problem, especially for stroke patients' treatments and dealing with real-time EMG sensors readings of muscles activity states and transfer between real-time Human motions to interface with rehabilitation robot agent or assisteddevice. The field of Artificial Intelligence and neural networks plays a critical role in modern intelligent control interfaces for robot devices. This paper presents a novel hybrid intelligent robot control that acts as human-robot interaction, where it depends on real-time EMG sensor patients data and extracted features along with estimated knee joint angles from Extended Kalman Filter method are used for training the intelligent controller using support vector machines trained with Adatron Learning algorithm for handling huge data values of sensors readings. Moreover, the proposed platform for rehabilitation robot agent is tested in the framework of the NAO Humanoid Robot agent along with Neurosolutions Toolkit and Matlab code. The average overall accuracy of the proposed intelligent motion SVM-EKF controller shows average high performance that approaches average 96% of knee motions classifications and also good performance for comparing Extended Kalman filter knee joint angles estimations and real EMG human knee joint angles in the framework of Human Walk Gait cycle. Also, the basic enhancement of proposing PSO optimization technique for robot knee motion is discussed for future improvements. The overall algorithm, methodology and experiments are presented in this paper along with future work.