Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression

Gaia Cavallo; Christopher P. Cop; M. Sartori; Alfred C. Schouten; John Lataire

doi:10.1007/978-3-030-70316-5_79

Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression

Gaia Cavallo^*, Christopher P. Cop, M. Sartori, Alfred C. Schouten, John Lataire

^*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

Joint impedance is a common way of representing human joint dynamics. Since ankle joint impedance varies within the gait cycle, time-varying system identification techniques can be used to estimate it. Commonly, time-varying system identification techniques assume repeatably of joint impedance over cyclic motions, without taking into consideration the inherent variability of human behavior. In this paper, a method that assumes smooth, cyclic joint impedance, yet allows for cycle-to-cycle variability, is proposed. The method was tested on isometric, cyclic experimental data from the ankle under conditions with a time variation comparable to the expected one during the gait cycle. The estimated model could describe the data with high accuracy (VAF of 94.96%) and retrieve realistic inertia, damping and stiffness parameters. The results provide motivation to further apply the method on experiments under dynamic conditions and to employ the proposed method as a tool for investigating the human joint dynamics during cyclic movements.

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	495-499
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_79
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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Cavallo, G., Cop, C. P., Sartori, M., Schouten, A. C., & Lataire, J. (2022). Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression. 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. 495-499). (Biosystems and Biorobotics; Vol. 28). Springer. https://doi.org/10.1007/978-3-030-70316-5_79

Cavallo, Gaia ; Cop, Christopher P. ; Sartori, M. et al. / Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression. 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. 495-499 (Biosystems and Biorobotics).

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title = "Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression",

abstract = "Joint impedance is a common way of representing human joint dynamics. Since ankle joint impedance varies within the gait cycle, time-varying system identification techniques can be used to estimate it. Commonly, time-varying system identification techniques assume repeatably of joint impedance over cyclic motions, without taking into consideration the inherent variability of human behavior. In this paper, a method that assumes smooth, cyclic joint impedance, yet allows for cycle-to-cycle variability, is proposed. The method was tested on isometric, cyclic experimental data from the ankle under conditions with a time variation comparable to the expected one during the gait cycle. The estimated model could describe the data with high accuracy (VAF of 94.96%) and retrieve realistic inertia, damping and stiffness parameters. The results provide motivation to further apply the method on experiments under dynamic conditions and to employ the proposed method as a tool for investigating the human joint dynamics during cyclic movements.",

author = "Gaia Cavallo and Cop, {Christopher P.} and M. Sartori and Schouten, {Alfred C.} and John Lataire",

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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Cavallo, G, Cop, CP, Sartori, M, Schouten, AC & Lataire, J 2022, Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression. 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. 495-499, ICNR 2020: International Conference on NeuroRehabilitation (Virtual), 13/10/20. https://doi.org/10.1007/978-3-030-70316-5_79

Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression. / Cavallo, Gaia; Cop, Christopher P.; Sartori, M. et al.
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. 495-499 (Biosystems and Biorobotics; Vol. 28).

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

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T1 - Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression

AU - Cavallo, Gaia

AU - Cop, Christopher P.

AU - Sartori, M.

AU - Schouten, Alfred C.

AU - Lataire, John

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 - Joint impedance is a common way of representing human joint dynamics. Since ankle joint impedance varies within the gait cycle, time-varying system identification techniques can be used to estimate it. Commonly, time-varying system identification techniques assume repeatably of joint impedance over cyclic motions, without taking into consideration the inherent variability of human behavior. In this paper, a method that assumes smooth, cyclic joint impedance, yet allows for cycle-to-cycle variability, is proposed. The method was tested on isometric, cyclic experimental data from the ankle under conditions with a time variation comparable to the expected one during the gait cycle. The estimated model could describe the data with high accuracy (VAF of 94.96%) and retrieve realistic inertia, damping and stiffness parameters. The results provide motivation to further apply the method on experiments under dynamic conditions and to employ the proposed method as a tool for investigating the human joint dynamics during cyclic movements.

AB - Joint impedance is a common way of representing human joint dynamics. Since ankle joint impedance varies within the gait cycle, time-varying system identification techniques can be used to estimate it. Commonly, time-varying system identification techniques assume repeatably of joint impedance over cyclic motions, without taking into consideration the inherent variability of human behavior. In this paper, a method that assumes smooth, cyclic joint impedance, yet allows for cycle-to-cycle variability, is proposed. The method was tested on isometric, cyclic experimental data from the ankle under conditions with a time variation comparable to the expected one during the gait cycle. The estimated model could describe the data with high accuracy (VAF of 94.96%) and retrieve realistic inertia, damping and stiffness parameters. The results provide motivation to further apply the method on experiments under dynamic conditions and to employ the proposed method as a tool for investigating the human joint dynamics during cyclic movements.

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SN - 978-3-030-70315-8

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BT - Converging Clinical and Engineering Research on Neurorehabilitation IV

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A2 - Akay, Metin

A2 - Pons, Jose L.

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ER -

Cavallo G, Cop CP, Sartori M, Schouten AC, Lataire J. Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression. 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. 495-499. (Biosystems and Biorobotics). doi: 10.1007/978-3-030-70316-5_79

Identification of Time-Varying Ankle Joint Impedance During Periodic Torque Experiments Using Kernel-Based Regression

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