Enhanced myoelectric control against arm position change with weighted recursive Gaussian process

Jung, MC; Chai, R; Zheng, J; Nguyen, H

Enhanced myoelectric control against arm position change with weighted recursive Gaussian process

Jung, MC Chai, R

Zheng, J Nguyen, H

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: Neural Computing and Applications, 2022, 34, (7), pp. 5015-5028
Issue Date:: 2022-04-01

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Field	Value	Language
dc.contributor.author	Jung, MC
dc.contributor.author	Chai, R https://orcid.org/0000-0002-1922-7024
dc.contributor.author	Zheng, J
dc.contributor.author	Nguyen, H https://orcid.org/0000-0003-3373-8178
dc.date.accessioned	2023-04-05T04:10:29Z
dc.date.available	2023-04-05T04:10:29Z
dc.date.issued	2022-04-01
dc.identifier.citation	Neural Computing and Applications, 2022, 34, (7), pp. 5015-5028
dc.identifier.issn	0941-0643
dc.identifier.issn	1433-3058
dc.identifier.uri	http://hdl.handle.net/10453/169176
dc.description.abstract	In the last decade, there has been huge advancement in biomechatronic systems by the integration of pattern recognition and regression algorithms. In many myoelectric control studies, high accuracy in estimating a subject’s wrist movement was reported by measuring electromyography (EMG) signal from subjects’ forearms. However, many algorithms suffer from limited robustness against undesired disturbance in the real-world environment. In particular, arm position change is an inevitable disturbance that results in severe degradation of performance. In this study, the weighted recursive Gaussian process (WRGP) is proposed to overcome this effect. In the algorithm, the noise variance is weighted by covariate shift adaptation, which is able to handle the uncertainties. WRGP is compared with the commonly used linear regression (LR) and multilayer perceptron (MLP). LR, MLP, and WRGP are trained with the EMG dataset acquired at an arm position and tested with the different EMG dataset acquired at another arm position. Also, WRGP with uniform weights and WRGP with the weights estimated from the covariate shift adaptation are compared. The results show that WRGP with uniform weights is more robust than LR and MLP regardless of the arm position (0.16 and 0.16 higher average R2 index, respectively). The performance of WRGP with the weights estimated from the covariate shift adaptation is significantly higher (p< 0.05) than the performance of LR and MLP (0.30 and 0.33 higher average R2 index, respectively).
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Neural Computing and Applications
dc.relation.isbasedon	10.1007/s00521-021-05743-y
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Enhanced myoelectric control against arm position change with weighted recursive Gaussian process
dc.type	Journal Article
utslib.citation.volume	34
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-05T04:10:28Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	34
utslib.citation.issue	7

Abstract:

In the last decade, there has been huge advancement in biomechatronic systems by the integration of pattern recognition and regression algorithms. In many myoelectric control studies, high accuracy in estimating a subject’s wrist movement was reported by measuring electromyography (EMG) signal from subjects’ forearms. However, many algorithms suffer from limited robustness against undesired disturbance in the real-world environment. In particular, arm position change is an inevitable disturbance that results in severe degradation of performance. In this study, the weighted recursive Gaussian process (WRGP) is proposed to overcome this effect. In the algorithm, the noise variance is weighted by covariate shift adaptation, which is able to handle the uncertainties. WRGP is compared with the commonly used linear regression (LR) and multilayer perceptron (MLP). LR, MLP, and WRGP are trained with the EMG dataset acquired at an arm position and tested with the different EMG dataset acquired at another arm position. Also, WRGP with uniform weights and WRGP with the weights estimated from the covariate shift adaptation are compared. The results show that WRGP with uniform weights is more robust than LR and MLP regardless of the arm position (0.16 and 0.16 higher average R2 index, respectively). The performance of WRGP with the weights estimated from the covariate shift adaptation is significantly higher (p< 0.05) than the performance of LR and MLP (0.30 and 0.33 higher average R2 index, respectively).

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http://hdl.handle.net/10453/169176