Joint torques estimation in human gait based on Gaussian process

Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions. The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process. The potential uses...

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Published in:Technology and health care Vol. 31; no. 1; p. 197
Main Authors: Yang, Jiantao, Wang, Zekai, Sun, Tairen
Format: Journal Article
Language:English
Published: Netherlands 01.01.2023
Subjects:
Biomechanical Phenomena - physiology
Gait - physiology
Humans
Lower Extremity - physiology
Torque
Walking - physiology
Walking Speed - physiology
GP
human gait
joint torque
mechanics
prostheses
robots
ISSN:1878-7401, 1878-7401
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Abstract Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions. The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process. The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs. The statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6 m/s). The results show that it is possible to estimate the joint torques at different scenarios.
AbstractList Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions.BACKGROUNDHuman gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions.The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process.OBJECTIVEThe goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process.The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs.METHODThe potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs.The statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6 m/s).RESULTSThe statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6 m/s).The results show that it is possible to estimate the joint torques at different scenarios.CONCLUSIONThe results show that it is possible to estimate the joint torques at different scenarios.
Human gait involves activities in nervous and musculoskeletal dynamics to modulate joint torques with time continuously for adapting to varieties of walking conditions. The goal of this paper is to estimate the joint torques of lower limbs in human gait based on Gaussian process. The potential uses of this study include optimization of exoskeleton assistance, control of the active prostheses, and modulating the joint torque for human-like robots. To achieve this, Gaussian process (GP) based data fusion algorithm is established with joint angles as the inputs. The statistic nature of the proposed model can explore the correlations between joint angles and joint torques, and enable accurate joint-torque estimations. Experiments were conducted for 5 subjects at three walking speed (0.8 m/s, 1.2 m/s, 1.6 m/s). The results show that it is possible to estimate the joint torques at different scenarios.
Author Yang, Jiantao
Sun, Tairen
Wang, Zekai
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  givenname: Jiantao
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  fullname: Yang, Jiantao
  organization: Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
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  givenname: Zekai
  surname: Wang
  fullname: Wang, Zekai
  organization: School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, China
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  givenname: Tairen
  surname: Sun
  fullname: Sun, Tairen
  organization: Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35964218$$D View this record in MEDLINE/PubMed
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human gait
joint torque
mechanics
prostheses
robots
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SubjectTerms Biomechanical Phenomena - physiology
Gait - physiology
Humans
Lower Extremity - physiology
Torque
Walking - physiology
Walking Speed - physiology
Title Joint torques estimation in human gait based on Gaussian process
URI https://www.ncbi.nlm.nih.gov/pubmed/35964218
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