Multi-DoF continuous estimation for wrist torques using stacked autoencoder

Abstract Human machine interface (HMI) based on surface electromyography (sEMG) promises to provide an intuitive and noninvasive way to interact with peripheral equipments, such as prostheses, exoskeletons, and robots. Most recently, advances in machine learning, especially in deep learning algorithms, present the capabilities in constructing complicated mapping functions. In this study, we construct a stacked autoencoder-based deep neural network (SAE-DNN) to continuously estimate multiple degrees-of-freedom (DoFs) kinetics of wrist from sEMG signals. During the experiments, high-density sEMG signals and multi-DoF wrist torques were simultaneously acquired under the guidance of a visual feedback system, with eight healthy subjects and an amputee recruited. Moreover, the estimation performance of SAE-DNN was compared with two of commonly used conventional regressors, linear regression (LR) and support vector regression (SVR). As a consequence, the results demonstrate the feasibility of this scheme and significant superiority of SAE-DNN over LR and SVR with higher R 2 values across all DoFs (SAE-DNN: 0.829 ± 0.050 , LR: 0.757 ± 0.075 , SVR: 0.751 ± 0.079 ). The outcomes of this study provide us with a perspective and a feasible scheme for simultaneous and proportional control.