Pattern Recognition of sEMG Recordings on Arm Muscles among Different Training Motions

The paper develops a prediction model based on the surface electromyography (sEMG) signals to recognize the five different arm-related motions. We collect 100 groups of the three-channels signals on Biceps, Triceps, Brachioradialis as the raw data set. Then we extract four features from the data to describe the characteristics of different motions in the data set. In the pre-processing stage, we compare three types of EMD-based filtration methods to denoise signals and choose the most efficient one, then use principle component analysis to reduce the dimension of the data. We use three different methods to classify and predict the processed data for a higher accuracy. The contributions are following: This project successfully automated the pattern recognition of arm-related motions. Moreover, the project generated a valuable data set; Compared performances of EMD, EEMD and CEEMDAN in sEMG signals; Found that FCNN is the most accurate algorithm in this project. Future works include obtain more data from more volunteers to expand the data set.