Multi-DoF continuous estimation for wrist torques using stacked autoencoder

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, prese...

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Veröffentlicht in:Biomedical signal processing and control Jg. 57; S. 101733
Hauptverfasser: Yu, Yang, Chen, Chen, Sheng, Xinjun, Zhu, Xiangyang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2020
Schlagworte:
Human machine interface
Simultaneous and proportional control
Stacked autoencoder
Simultaneous and proportional control
Stacked autoencoder
Human machine interface
ISSN:1746-8094, 1746-8108
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Zusammenfassung: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 R2 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.
ISSN:1746-8094
1746-8108
DOI:10.1016/j.bspc.2019.101733