Surface Electromyography Based Estimation of Knee Joint Angle by Using Correlation Dimension of Wavelet Coefficient

In this paper, we propose a regression model to establish a relationship between surface electromyography (sEMG) and knee joint angles. In this model, the correlation dimension of wavelet coefficient (WCCD) and an Elman network are developed for the model estimation. In our experiment, the sEMG signals were recorded from five muscles concerned with knee joint motion, and knee joint angles were simultaneously recorded by a Codamotion system. First, we used a feature extraction method based on WCCD to extract optimal feature vectors from multichannel sEMG signals. Then, the Elman network was used to map the optimal sEMG features to the knee joint angles. The results show that the features extracted from the multichannel sEMG signals using the WCCD method proposed in this paper outperform the time-domain and frequency-domain methods. Our method is expected to be applied to intelligent prosthetics, exoskeleton robots, and medical rehabilitation robots.