TY - GEN
T1 - SEMG-based neural-musculoskeletal model for human-robot interface
AU - Tao, Ran
AU - Xie, Shane
AU - Zhang, Yanxin
AU - Pau, James W.L.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/20
Y1 - 2014/10/20
N2 - Neural-musculoskeletal models play a significant role in the interactions between human and robotic devices. Surface Electromyography (sEMG) can effectively measure the electric signal from human muscle and provide useful information for improving the accuracy of human-machine interfaces. This paper summarizes three main sEMG-based research methods at present, establishes the flowchart for sEMG-based musculoskeletal models, and theoretically analyzes the key methods of this interface (which includes sEMG signal filtering, muscle and skeleton model analysis and parameter setting). Also, by using the elbow joint as an example, this paper gathers bicep and tricep signal from experiments, gains muscle activations through Matlab/Simulink software, and simulates joint movement via forward dynamics in OpenSim. By tuning key musculoskeletal parameters, the model's root mean square error (RMSE) for single flexion-extension movement is reduced to 3.98-8.5 degree, showing the feasibility of the potential of using the interface for many applications.
AB - Neural-musculoskeletal models play a significant role in the interactions between human and robotic devices. Surface Electromyography (sEMG) can effectively measure the electric signal from human muscle and provide useful information for improving the accuracy of human-machine interfaces. This paper summarizes three main sEMG-based research methods at present, establishes the flowchart for sEMG-based musculoskeletal models, and theoretically analyzes the key methods of this interface (which includes sEMG signal filtering, muscle and skeleton model analysis and parameter setting). Also, by using the elbow joint as an example, this paper gathers bicep and tricep signal from experiments, gains muscle activations through Matlab/Simulink software, and simulates joint movement via forward dynamics in OpenSim. By tuning key musculoskeletal parameters, the model's root mean square error (RMSE) for single flexion-extension movement is reduced to 3.98-8.5 degree, showing the feasibility of the potential of using the interface for many applications.
KW - Forward Dynamic
KW - Interface
KW - Neuromuscular Model
KW - Simulation Introduction
KW - sEMG
UR - https://www.scopus.com/pages/publications/84912062262
U2 - 10.1109/ICIEA.2014.6931317
DO - 10.1109/ICIEA.2014.6931317
M3 - 会议稿件
AN - SCOPUS:84912062262
T3 - Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014
SP - 1039
EP - 1044
BT - Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014
Y2 - 9 June 2014 through 11 June 2014
ER -