Hand Gesture Recognition Based on Deep Neural Network and sEMG Signal The physiological electrical signal of human body is the direct response from human behavior intention. By analyzing and interpreting the physiological electrical signal of human body, human subject consciousness can be effectively recognized. In order to enable people with physical disabilities to effectively control peripherals according to their own intentions, this research is based on the classification and recognition of sEMG hand movements. The position of sEMG electrode on the muscle is determined. sEMG signals of 8 kinds of common hand movements in daily life are successfully collected and data collection of sEMG signals is completed. The VGG16 is used to construct the convolutional neural network structure suitable for sEMG gesture recognition. At the same time, a highly biomimetic robotic hand is built according to the bone structure of human hand to complete 8 gestures as an actuator, and STM32 microprocessor is used to control it. Through the comparison and analysis with two VGG16 networks with different convolution kernel sizes, the results show that the VGG16 network structure with convolution kernel size of 3xc3x973 adopted by this method has a high accuracy for the recognition of 8 kinds of hand movements.

DFNNbased Gesture Recognition with the Shift and Damage of the HDsEMG Electrodes The surface electromyography (sEMG) signals can be used to identify the activity intention. Recently, Highdensity (HD) electrodes have been widely used to record sEMG signals for myoelectric control. However, the problem of the electrodes shift or damage in dons and doff often happens, it will have a greatly bad influence on the accuracy and stability of gesture recognition. In this paper, we propose a deep fast neural network (DFNN) to attenuate these issues by utilizing the spatialtemporal features of sEMG signals. The performance of the proposed model is compared with the locality sensitivity analysis (LSDA) and long shortterm memory (LSTM), and they are all tested to classify nine gestures in these situations including the electrodes shift (10mm), shift (10mm) and damage (6). Without pretraining, the proposed DFNN model can high average 93.35%, and the error between individuals is less than 0.045 when the electrodes shift and damage.