Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot.

Zhou, J; Peng, H; Su, S; Song, R

Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot.

Zhou, J Peng, H Su, S

Song, R

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Biomed Eng, 2023, 70, (6), pp. 1858-1868
Issue Date:: 2023-06

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Field	Value	Language
dc.contributor.author	Zhou, J
dc.contributor.author	Peng, H
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852
dc.contributor.author	Song, R
dc.date.accessioned	2024-04-22T05:36:17Z
dc.date.available	2024-04-22T05:36:17Z
dc.date.issued	2023-06
dc.identifier.citation	IEEE Trans Biomed Eng, 2023, 70, (6), pp. 1858-1868
dc.identifier.issn	0018-9294
dc.identifier.issn	1558-2531
dc.identifier.uri	http://hdl.handle.net/10453/178198
dc.description.abstract	Compliance control is crucial for physical human-robot interaction, which can enhance the safety and comfort of robot-assisted rehabilitation. In this study, we designed a spatiotemporal compliance control strategy for a new self-designed wearable lower limb rehabilitation robot (WLLRR), allowing the users to regulate the spatiotemporal characteristics of their motion. The high-level trajectory planner consists of a trajectory generator, an interaction torque estimator, and a gait speed adaptive regulator, which can provide spatial and temporal compliance for the WLLRR. A radial basis function neural network adaptive controller is adopted as the low-level position controller. Over-ground walking experiments with passive control, spatial compliance control, and spatiotemporal compliance control strategies were conducted on five healthy participants, respectively. The results demonstrated that the spatiotemporal compliance control strategy allows participants to adjust reference trajectory through physical human-robot interaction, and can adaptively modify gait speed according to participants' motor performance. It was found that the spatiotemporal compliance control strategy could provide greater enhancement of motor variability and reduction of interaction torque than other tested control strategies. Therefore, the spatiotemporal compliance control strategy has great potential in robot-assisted rehabilitation training and other fields involving physical human-robot interaction.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Biomed Eng
dc.relation.isbasedon	10.1109/TBME.2022.3230784
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Biomedical Engineering
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.mesh	Humans
dc.subject.mesh	Gait
dc.subject.mesh	Lower Extremity
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Robotics
dc.subject.mesh	Walking
dc.subject.mesh	Wearable Electronic Devices
dc.subject.mesh	Exercise Therapy
dc.subject.mesh	Lower Extremity
dc.subject.mesh	Humans
dc.subject.mesh	Gait
dc.subject.mesh	Exercise Therapy
dc.subject.mesh	Walking
dc.subject.mesh	Robotics
dc.subject.mesh	Wearable Electronic Devices
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Humans
dc.subject.mesh	Gait
dc.subject.mesh	Lower Extremity
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Robotics
dc.subject.mesh	Walking
dc.subject.mesh	Wearable Electronic Devices
dc.subject.mesh	Exercise Therapy
dc.title	Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot.
dc.type	Journal Article
utslib.citation.volume	70
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
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
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-22T05:36:15Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	70
utslib.citation.issue	6

Abstract:

Compliance control is crucial for physical human-robot interaction, which can enhance the safety and comfort of robot-assisted rehabilitation. In this study, we designed a spatiotemporal compliance control strategy for a new self-designed wearable lower limb rehabilitation robot (WLLRR), allowing the users to regulate the spatiotemporal characteristics of their motion. The high-level trajectory planner consists of a trajectory generator, an interaction torque estimator, and a gait speed adaptive regulator, which can provide spatial and temporal compliance for the WLLRR. A radial basis function neural network adaptive controller is adopted as the low-level position controller. Over-ground walking experiments with passive control, spatial compliance control, and spatiotemporal compliance control strategies were conducted on five healthy participants, respectively. The results demonstrated that the spatiotemporal compliance control strategy allows participants to adjust reference trajectory through physical human-robot interaction, and can adaptively modify gait speed according to participants' motor performance. It was found that the spatiotemporal compliance control strategy could provide greater enhancement of motor variability and reduction of interaction torque than other tested control strategies. Therefore, the spatiotemporal compliance control strategy has great potential in robot-assisted rehabilitation training and other fields involving physical human-robot interaction.

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http://hdl.handle.net/10453/178198