Continuous Description of Human 3D Motion Intent through Switching Mechanism

Huang, Y; Song, R; Argha, A; Savkin, AV; Celler, BG; Su, SW

Continuous Description of Human 3D Motion Intent through Switching Mechanism

Huang, Y

Song, R Argha, A

Savkin, AV

Celler, BG

Su, SW

Permalink

Publication Type:: Journal Article
Citation:: IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28 (1), pp. 277 - 286
Issue Date:: 2020-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

The embargo period expires on 1 Jan 2022

Adobe PDF

Download ProofAdobe PDF (2 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Huang, Y https://orcid.org/0000-0002-4312-0948	en_US
dc.contributor.author	Song, R	en_US
dc.contributor.author	Argha, A https://orcid.org/0000-0002-8276-9774	en_US
dc.contributor.author	Savkin, AV https://orcid.org/0000-0001-9390-6634	en_US
dc.contributor.author	Celler, BG https://orcid.org/0000-0003-3790-2895	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2020-01-01	en_US
dc.identifier.citation	IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28 (1), pp. 277 - 286	en_US
dc.identifier.issn	1534-4320	en_US
dc.identifier.uri	http://hdl.handle.net/10453/138625
dc.description.abstract	© 2001-2011 IEEE. Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.	en_US
dc.relation.ispartof	IEEE Transactions on Neural Systems and Rehabilitation Engineering	en_US
dc.relation.isbasedon	10.1109/TNSRE.2019.2949203	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject.classification	Biomedical Engineering	en_US
dc.title	Continuous Description of Human 3D Motion Intent through Switching Mechanism	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	28	en_US
utslib.for	0903 Biomedical Engineering	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-01-01T00:00:00+1100
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

© 2001-2011 IEEE. Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/138625