An EMG-driven neuromuscular interface for human elbow joint

Pau, James W. L.; Saini, Harnoor; Xie, Shane; Pullan, Andrew J.; Mallinson, Gordon D.

doi:10.1109/biorob.2010.5627758

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…It combines sEMG dels and a musculoskeletal haracter of weakness, lows unavoidable to introduce n sources of noise include background noise, highial motion. To cancel the [4,5] has been used for ctivations from the sEMG C offsets and low-frequency ectrodes. Based on the type frequency is generally from ave a zero phase delay, so it me.…”

Section: ) Normalizationmentioning

confidence: 99%

sEMG-based neural-musculoskeletal model for human-robot interface

Tao

Xie

Zhang

et al. 2014

2014 9th IEEE Conference on Industrial Electronics and Applications

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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 sEMGbased 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.

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Section: ) Normalizationmentioning

confidence: 99%

sEMG-based neural-musculoskeletal model for human-robot interface

Tao

Xie

Zhang

et al. 2014

2014 9th IEEE Conference on Industrial Electronics and Applications

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“…To achieve this, the information bandwidth of input should be expanded, the efficiency of input should be improved and the naturalness of human-robot interaction should be enhanced. Currently, the information sources for the control of prosthetic upper limb are pictures from computer vision, signals from generic sensor, Electroencephalogram (EEG), and EMG (Cavallaro et al, 2006;Pau et al, 2010;Cososchi et al, 2006;Reyes et al, 2012;Manal et al, 2002;Prentice et al, 2001;Lloyd and Besier, 2003;Moeslund and Granum, 2001) • Input from computer vision…”

Section: Emgmentioning

confidence: 99%

“…This review mainly covers the development of EMG-based neuromuscular interfaces (Pau et al, 2010(Pau et al, , 2012a(Pau et al, , 2012b) and related fields. Section 2 briefly gives an overview of the biological system as a biological underpinning for our research.…”

Section: Introductionmentioning

confidence: 99%

Review of EMG-based neuromuscular interfaces for rehabilitation: elbow joint as an example

Tao

Xie

Zhang

et al. 2013

IJBBR

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Assistive robots have made great contributions to disabled people in physiotherapy and rehabilitation areas. The interface between patients and medical devices plays a significant role for patients to operate these kinds of robots. This review introduces the current research and development of neuromuscular interfaces, especially the new research directions with special focus on modelling of musculoskeletal systems for interfacing purposes. The paper also summarises the function and prominent advantage of using surface electromyography (sEMG) signals for the interface. The elbow joint was used as an example to go through the working steps of both human biological systems and neuromuscular interfaces. Further developments were also discussed to improve the interface to meet medical demands.

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