sEMG recognition has been used extensively in prosthetic device control, human-assisting manipulators and sign language recognition, etc. However, the sEMG recognition model, trained with one subject's sEMG data, is not applicable to the other subjects, which hinders the practical application of myoelectric interfaces immensely. In this paper, a sEMG recognition method which is applicable to multi-users is proposed. Firstly, single channel sEMG is decomposed into 30 MUAPTs, which includes four steps: two-order differential filter, threshold calculation, spike detection and hierarchical clustering. Secondly, the MUAPTs are updated with the templates orthogonalization; and Deep Boltzman Machine is employed to classify the MUAPTs into five classes corresponding to the predefined five gestures. Six participants participated in this experiment to validate the effectiveness of the proposed method. Results indicated that this method can achieve a mean accuracy of 81.5%.

Estimation of the joint angle from the surface electromyography (sEMG) is a quite complex task due to the complicated relationship between the kinematical variables and the raw sEMG. In this paper, we build a sEMG-to-motion model with the artificial neural network (ANN). EMG features, including Integral of absolute value (IAV), Zero crossing (ZC), Auto-regression coefficients (ARC), Median frequency (MDF), are extracted as the input of the ANN, and the output of the ANN is the operator's elbow joint angle and the wrist motion. In addition, a 3D upper extremity model, which is built in SolidWorks and then transformed into MATLAB, will imitate the operator's motion simultaneously with the estimations of the ANN. Thus, we accomplish a virtual reality system to realize the real-time simulation and validate the effectiveness of the sEMG-to-motion model. Experiment results show that the system achieves well in model accuracy, hardware compatibility and real time performance with a small mean square error of 1.921 degrees.

Electromyography (EMG) has been widely used as control commands for prosthesis, powered exoskeletons and rehabilitative robots. In this paper, an EMG-driven state space model is developed to estimate continuous joint angular displacement and velocity, demonstrated by elbow flexion/ extension. The model combines the Hill-based muscle model with the forward dynamics of joint movement, in which kinematic variables are expressed as a function of neural activation levels. EMG features including integral of absolute value and waveform length are then extracted, and two quadratic equations which associate the kinematic variables with EMG features are constructed to represent the measurement equation. The proposed model are verified by extensively experiments, where the angular movements of human elbow joint are estimated only using the EMG signals, and the estimations are compared with the IMU measurements to validate the accuracy. As a demonstration, a robotic arm is commanded to follow the human elbow movement estimated by the proposed model, which shows the possibility of EMG-based robotic assisted rehabilitation.