报告人：刘汉东 18722281 机械电子工程（导师：钱晋武 教授）
内容简介：In recent years, the analysis of surface electromyography (sEMG) signals by feature engineering and machine learning has developed rapidly. However, when feature engineering is applied to feature extraction of sEMG signals, important feature information in the signals will inevitably be omitted, which will reduce the performance of signal analysis and recognition. Therefore, this paper proposes a method to complete classication of sEMG hand movements based on convolutional neural network (CNN) and stacking ensemble learning. In this method, a primary classier based on CNN is designed to extract sEMG data features, which avoid omission of important feature information. A secondary classier based on the stacking method is designed to integrate three primary classiers trained with time domain, frequency domain and time-frequency domain data of the sEMG signal respectively. Then, several experiments on NinaPro DB5 dataset is performed to evaluate the proposed models. When the window length is 200ms, primary classier is trained and tested with the sEMG signal data divided by the 80ms, 100ms, and 125ms sliding length. The best accuracy can reach 71%. The primary classier and the secondary classier trained and tested with sEMG signal data divided by window lengths of 200ms and 300ms in the case of a sliding length of 100ms. When the window length is 200ms, the best primary classier accuracy and the best secondary classier accuracy can be 70.92% and 72.09%, respectively. On the window length of 300ms,
the best primary classier accuracy and the best secondary classier accuracy can reach 75.02% and 76.02%, respectively. Finally, the model designed is compared with Linear Discriminant Analysis (LDA), Long Short Term Memory-CNN (LCNN), Support Vector Machine (SVM), and Random Forests. Under the same conditions, the average accuracy of the secondary classier is 11.5%, 13.6%. and 10.1% higher than LDA, SVM, and LCNN, respectively. Also, the average accuracy rate is 3.05% higher than SVM and Random Forests.