Improving robotic hand control via adaptive Fuzzy-PI controller using classification of EMG signals.

Robotic or prosthetic organs are designed to have the maximum similarity to human organs. This paper aims to improve robotic hand control via an adaptive Fuzzy-PI controller using EMG signals. The data is collected from the FDS and FPL muscles of the forearm of five individuals who performed eight movements. Then, appropriate filters are used to eliminate the noise of the signals, and MAV, VAR, and SE features are extracted. Based on MAV and VAR, classification is carried out using DA, KNN, and SVM. With an average accuracy, specificity, and sensitivity of 90.69%, 94.64%, and 62.10%, SVM is a better choice for movement detection. Following the movement detection by SVM, an appropriate reference signal is sent to the controller. The reference signal is the angle change of the fingers during the movement. All the eight gestures are modeled in a new way through these angles. The adaptive fuzzy-PI controller is used to control a robotic hand model with fifteen degrees of freedom. It has the advantages of learning from human experiences and adapting to environmental changes. The performance of the controller is evaluated in two ways. One is the comparison of the fuzzy-PI with the PI by RMSE. The average RMSE for eight movements using the fuzzy-PI is 1.6067, and for the PI, 5.0082. These results show that the fuzzy-PI controller performs better than the PI. Another new evaluation way presented in this paper is comparing the EMG signal features with the robotic hand movement signal features in terms of RMSE. The small RMSE values indicate that the EMG signal and robotic hand movement data features are significantly similar. Therefore, it can be concluded that the robotic hand controlled by the proposed controller is notably identical to the human hand.