Quantifying Joint Stiffness During Movement: A Quantitative Comparison of Time-Varying System Identification Methods

Mark van de Ruit; Winfred Mugge; Alfred C. Schouten

doi:10.1007/978-3-030-70316-5_82

Quantifying Joint Stiffness During Movement: A Quantitative Comparison of Time-Varying System Identification Methods

Mark van de Ruit^*, Winfred Mugge, Alfred C. Schouten

^*Corresponding author for this work

Biomechatronics & Human-Machine Control

Research output: Chapter in Book/Conference proceedings/Edited volume › Chapter › Scientific › peer-review

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Abstract

Careful control of joint impedance, or dynamic joint stiffness, is crucial for successful performance of movement. Time-varying system identification (TV-SysID) enables quantification of joint impedance during movement. Several TV-SysID methods exist, but have never been systematically compared. Here, we simulate time-varying joint behavior and propose three performance metrics that enable to quantify and compare TV-SysID methods. Time-varying joint stiffness is simulated using a square wave and subsequently estimated with three TV-SysID methods: the ensemble, short data segment, and basis impulse response function method. These methods were compared based on (1) bias with respect to the simulated joint stiffness, (2) random error across 100 simulation trials, and (3) maximum adaptation speed in joint stiffness that can be captured. This approach revealed that each TV-SysID method has its own unique properties. The simulation method and performance metrics pave the way for developing a framework to quantify the strengths and weaknesses of TV-SysID algorithms for estimating joint impedance.

Original language	English
Title of host publication	Converging Clinical and Engineering Research on Neurorehabilitation IV
Subtitle of host publication	Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020
Editors	Diego Torricelli, Metin Akay, Jose L. Pons
Publisher	Springer
Pages	513-518
ISBN (Electronic)	978-3-030-70316-5
ISBN (Print)	978-3-030-70315-8
DOIs	https://doi.org/10.1007/978-3-030-70316-5_82
Publication status	Published - 2022
Event	ICNR 2020: International Conference on NeuroRehabilitation (Virtual) - Duration: 13 Oct 2020 → 16 Oct 2020

Publication series

Name	Biosystems and Biorobotics
Volume	28
ISSN (Print)	2195-3562
ISSN (Electronic)	2195-3570

Conference

Conference	ICNR 2020: International Conference on NeuroRehabilitation (Virtual)
Period	13/10/20 → 16/10/20

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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van de Ruit, M., Mugge, W., & Schouten, A. C. (2022). Quantifying Joint Stiffness During Movement: A Quantitative Comparison of Time-Varying System Identification Methods. In D. Torricelli, M. Akay, & J. L. Pons (Eds.), Converging Clinical and Engineering Research on Neurorehabilitation IV: Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020 (pp. 513-518). (Biosystems and Biorobotics; Vol. 28). Springer. https://doi.org/10.1007/978-3-030-70316-5_82

van de Ruit, Mark ; Mugge, Winfred ; Schouten, Alfred C. / Quantifying Joint Stiffness During Movement : A Quantitative Comparison of Time-Varying System Identification Methods. Converging Clinical and Engineering Research on Neurorehabilitation IV: Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020. editor / Diego Torricelli ; Metin Akay ; Jose L. Pons. Springer, 2022. pp. 513-518 (Biosystems and Biorobotics).

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abstract = "Careful control of joint impedance, or dynamic joint stiffness, is crucial for successful performance of movement. Time-varying system identification (TV-SysID) enables quantification of joint impedance during movement. Several TV-SysID methods exist, but have never been systematically compared. Here, we simulate time-varying joint behavior and propose three performance metrics that enable to quantify and compare TV-SysID methods. Time-varying joint stiffness is simulated using a square wave and subsequently estimated with three TV-SysID methods: the ensemble, short data segment, and basis impulse response function method. These methods were compared based on (1) bias with respect to the simulated joint stiffness, (2) random error across 100 simulation trials, and (3) maximum adaptation speed in joint stiffness that can be captured. This approach revealed that each TV-SysID method has its own unique properties. The simulation method and performance metrics pave the way for developing a framework to quantify the strengths and weaknesses of TV-SysID algorithms for estimating joint impedance.",

author = "{van de Ruit}, Mark and Winfred Mugge and Schouten, {Alfred C.}",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; ICNR 2020: International Conference on NeuroRehabilitation (Virtual) ; Conference date: 13-10-2020 Through 16-10-2020",

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van de Ruit, M , Mugge, W & Schouten, AC 2022, Quantifying Joint Stiffness During Movement: A Quantitative Comparison of Time-Varying System Identification Methods. in D Torricelli, M Akay & JL Pons (eds), Converging Clinical and Engineering Research on Neurorehabilitation IV: Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020. Biosystems and Biorobotics, vol. 28, Springer, pp. 513-518, ICNR 2020: International Conference on NeuroRehabilitation (Virtual), 13/10/20. https://doi.org/10.1007/978-3-030-70316-5_82

Quantifying Joint Stiffness During Movement: A Quantitative Comparison of Time-Varying System Identification Methods. / van de Ruit, Mark ; Mugge, Winfred ; Schouten, Alfred C.
Converging Clinical and Engineering Research on Neurorehabilitation IV: Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020. ed. / Diego Torricelli; Metin Akay; Jose L. Pons. Springer, 2022. p. 513-518 (Biosystems and Biorobotics; Vol. 28).

Research output: Chapter in Book/Conference proceedings/Edited volume › Chapter › Scientific › peer-review

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AU - van de Ruit, Mark

AU - Mugge, Winfred

AU - Schouten, Alfred C.

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2022

Y1 - 2022

N2 - Careful control of joint impedance, or dynamic joint stiffness, is crucial for successful performance of movement. Time-varying system identification (TV-SysID) enables quantification of joint impedance during movement. Several TV-SysID methods exist, but have never been systematically compared. Here, we simulate time-varying joint behavior and propose three performance metrics that enable to quantify and compare TV-SysID methods. Time-varying joint stiffness is simulated using a square wave and subsequently estimated with three TV-SysID methods: the ensemble, short data segment, and basis impulse response function method. These methods were compared based on (1) bias with respect to the simulated joint stiffness, (2) random error across 100 simulation trials, and (3) maximum adaptation speed in joint stiffness that can be captured. This approach revealed that each TV-SysID method has its own unique properties. The simulation method and performance metrics pave the way for developing a framework to quantify the strengths and weaknesses of TV-SysID algorithms for estimating joint impedance.

AB - Careful control of joint impedance, or dynamic joint stiffness, is crucial for successful performance of movement. Time-varying system identification (TV-SysID) enables quantification of joint impedance during movement. Several TV-SysID methods exist, but have never been systematically compared. Here, we simulate time-varying joint behavior and propose three performance metrics that enable to quantify and compare TV-SysID methods. Time-varying joint stiffness is simulated using a square wave and subsequently estimated with three TV-SysID methods: the ensemble, short data segment, and basis impulse response function method. These methods were compared based on (1) bias with respect to the simulated joint stiffness, (2) random error across 100 simulation trials, and (3) maximum adaptation speed in joint stiffness that can be captured. This approach revealed that each TV-SysID method has its own unique properties. The simulation method and performance metrics pave the way for developing a framework to quantify the strengths and weaknesses of TV-SysID algorithms for estimating joint impedance.

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DO - 10.1007/978-3-030-70316-5_82

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SN - 978-3-030-70315-8

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van de Ruit M , Mugge W , Schouten AC. Quantifying Joint Stiffness During Movement: A Quantitative Comparison of Time-Varying System Identification Methods. In Torricelli D, Akay M, Pons JL, editors, Converging Clinical and Engineering Research on Neurorehabilitation IV: Proceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020. Springer. 2022. p. 513-518. (Biosystems and Biorobotics). doi: 10.1007/978-3-030-70316-5_82

Quantifying Joint Stiffness During Movement: A Quantitative Comparison of Time-Varying System Identification Methods

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