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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. Index Terms-EMG, hill-based muscle model, joint angular movement, state space model. I.

Electromyography (EMG) has some good abilities for bionic mechanical hand's control and researchers have proposed many kinds of methods for EMG classification. Principal Components Analysis (PCA) which is an ideal tool for dimension reduction tool was introduced for EMG classification. Linear Discriminant Analysis (LDA) performs outstandingly on classification. This paper does a comparative study on PCA and LDA for EMG classification, mainly including LDA for raw EMG, LDA for features, PCA and LDA for raw EMG and PCA and LDA for features. Here five time-domain features and four frequency-domain features are selected. The five hand motions including hand closing, hand opening, index finger pinching, middle finger pinching and hand relaxing are selected for classification. The result shows PCA and LDA for features obtain 99.0% motion success rate and 99.8% success rate of classification. The bionic mechanical hand got a good performance.