Design of sEMG gesture recognition control system based on DTW algorithm
The human body can produce weak bioelectric signals during movement,which contains a large amount of control information.In order to use the information from bioelectric signals to control the movement of robotic arms,a sEMG(surface electromyography)gesture recognition control system based on DTW(dynamic time warping)algorithm is proposed,which can be used to filter and amplify the collected original signal.In order to determine the effective sEMG,the moving average method is used to partition the processed signal.The MAV(mean absolute value)is used to extract the valid segment data from data fragments,the DTW algorithm is used to fuse three sEMG,and the similarity between the sample and the model is calculated,so as to realize the gesture recognition.The recognized signal is used to send the control command by means of the wireless module to control the action of the robotic arm.The proposed algorithm is used to create the optimal feature model by combining with 6 gesture classification models.The experimental results show that the average accuracy of the gesture recognition using the DTW algorithm is 93.752%,and the average model matching rate of 6 gestures can reach 92%,achieving the precise control of the robotic arm by sEMG.It proves that the gesture recognition of the proposed method is more accurate than traditional threshold controlled switches.
万方数据
维普
