Abstract: In recent years, sEMG (surface electromyography) signals have been increasingly used to operate wearable devices. The development of mechanical lower limbs or exoskeletons controlled by the nervous system requires greater accuracy in recognizing lower limb activity. There is less research on devices to assist the human body in uphill movements. However, developing controllers that can accurately predict and control human upward movements in real-time requires the employment of appropriate signal pre-processing methods and prediction algorithms. For this purpose, this paper investigates the effects of various sEMG pre-processing methods and algorithms on the prediction results. This investigation involved ten subjects (five males and five females) with normal knee joints. The relevant data of the subjects were collected on a constructed ramp. To obtain feature values that reflect the gait characteristics, an improved PCA algorithm based on the kernel method is proposed for dimensionality reduction to remove redundant information. Then, a new model (CNN?+?LSTM) was proposed to predict the knee joint angle. Multiple approaches were utilized to validate the superiority of the improved PCA method and CNN-LSTM model. The feasibility of the method was verified by analyzing the gait prediction results of different subjects. Overall, the prediction time of the method was 25 ms, and the prediction error was 1.34?±?0.25 deg. By comparing with traditional machine learning methods such as BP (backpropagation) neural network, RF (random forest), and SVR (support vector machine), the improved PCA algorithm processed data performed the best in terms of convergence time and prediction accuracy in CNN-LSTM model. The experimental results demonstrate that the proposed method (improved PCA?+?CNN-LSTM) effectively recognizes lower limb activity from sEMG signals. For the same data input, the EMG signal processed using the improved PCA method performed better in terms of prediction results. This is the first step toward myoelectric control of aided exoskeleton robots using discrete decoding. The study results will lead to the future development of neuro-controlled mechanical exoskeletons that will allow troops or disabled individuals to engage in a greater variety of activities.

Key words: CNN , · LSTM , · Exoskeleton , · sEMG , · Knee trajectory , · PCA , · Bionic robot