Research On Gesture Recognition Based On Surface EMG Signal

Surface EMG(s EMG)is the bioelectric signal produced by the muscle during human movement,which reflects the state of muscle movement.Surface EMG signal is widely used in rehabilitation medicine,sports medicine,intelligent robot and other fields due to its easy acquisition and convenience.In this paper,the gesture recognition research based on surface EMG signals is studied.According to the recognition results,the nursing bed can be controlled to complete the corresponding movements,which can not only meet the urgent needs of physically disabled patients for the autonomous control of intelligent nursing bed,but also provide more reliable and humanized auxiliary rehabilitation equipment in the field of rehabilitation medicine.Therefore,it has very important research significance and application value,The main work of this paper is as follows:(1)The generation mechanism and characteristics of surface EMG signals are introduced,and the way to collect surface EMG signals is determined,which is noninvasive collection,and the muscle and skin surface EMG signals of the experimenter are collected by surface electrode patch.(2)A total of four kinds of gestures are designed in the experiment,and the initial position of the surface EMG signals is segmenting by window analysis method to obtain the hand movement signals.The segmentation surface EMG signals are filtered by fifth order Butterworth bandpass filter,and then the filtered surface EMG signals are denoised by CEEMDAN-wavelet threshold method.Finally,the surface EMG signals after noise reduction are extracted in time domain and frequency domain,and the principal component analysis method(PCA)is used to carry out feature dimensionreduction processing on the extracted EMG signals,which played an important role in improving the classification accuracy of the following gesture classification.(3)The nearest neighbor algorithm,BP neural network algorithm and support vector machine(SVM)algorithm are used to classify and recognize EMG features of different gesture movement patterns.The results showed that the SVM model had the best classification effect,and the recognition rate is 96.43%.Finally,the support vector machine algorithm is chosen as the application research of gesture recognition.(4)The experiment of gesture recognition and nursing bed control is designed.The experiment of nursing bed control based on surface EMG signals is carried out on5 subjects.Four kinds of gestures are used to control the movement of nursing bed,and the accuracy and real-time performance are evaluated.The experimental results show that the nursing bed can complete the motion control under the gesture recognition,and can help the hemiplegia patients complete the autonomous rehabilitation training.