DocumentCode :
128571
Title :
sEMG-based neural-musculoskeletal model for human-robot interface
Author :
Ran Tao ; Shane Xie ; Yanxin Zhang ; Pau, James W. L.
Author_Institution :
Mech. Eng., Univ. of Auckland, Auckland, New Zealand
fYear :
2014
fDate :
9-11 June 2014
Firstpage :
1039
Lastpage :
1044
Abstract :
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.
Keywords :
bone; control engineering computing; electromyography; filtering theory; flowcharting; human-robot interaction; mean square error methods; medical robotics; medical signal processing; Matlab/Simulink software; OpenSim; RMSE; bicep signal; elbow joint; electric signal measurement; flowchart; forward dynamics; human muscle; human-machine interfaces; human-robot interface; human-robotic devices interactions; joint movement; muscle activations; musculoskeletal parameters; root mean square error; sEMG signal filtering; sEMG-based neural-musculoskeletal model; single flexion-extension movement; skeleton model analysis; surface electromyography; tricep signal; Elbow; Electromyography; Force; Joints; Mathematical model; Neuromuscular; Forward Dynamic; Interface; Neuromuscular Model; Simulation Introduction; sEMG;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Electronics and Applications (ICIEA), 2014 IEEE 9th Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4799-4316-6
Type :
conf
DOI :
10.1109/ICIEA.2014.6931317
Filename :
6931317
Link To Document :
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