sEMG-Based Control of Prosthesis Hand Using LSTM Classification and SMC

In this paper, an approach for controlling an prosthesis hand using surface Electromyography (sEMG) signal, artificial neural network (ANN) and sliding mode control (SMC) is presented. An artificial hand mechanism also known as a prosthetic hand or bionic hand, is device designed to replace the function of a missing or non-functioning hand. surface Electromyography (sEMG) were used to detect hand muscle activities which were interpreted into six specific hand movements (gestures). By sensing the activities of two muscle groups of the user’s arm, the fingers in the robotic hand were controlled to follow specific trajectories. The classification of the sEMG signals into one of the six gestures was done using Long Short-Term Memory (LSTM) neural network that was trained from a dataset collected from five people. To improve the performance of the LSTM during classification, feature extraction operation of the sEMG signal was performed during training and classification. This increased the accuracy of the neural network from 52.3%, using raw data, to 99.7%, using extracted features. Once the required hand gesture was identified from the sEMG signal, interpretation of the gesture into individual fingers joint angles was done using cubic polynomial path-planning algorithm. These joint angle trajectories were used to command the robotic hand using Sliding Mode Control (SMC) controller. The use of SMC instead of other existing controllers such as PIDs also improved parameter uncertainty handling capabilities of the robotic had control system by a significant amount.