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

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

1 Citation (Scopus)

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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
Number of pages	5
ISBN (Electronic)	978-3-030-70316-5
ISBN (Print)	978-3-030-70315-8, 978-3-030-70318-9
DOIs	https://doi.org/10.1007/978-3-030-70316-5_79
Publication status	Published - 2 Oct 2022
Event	5th International Conference on NeuroRehabilitation, ICNR 2020 - Virtual Event Duration: 13 Oct 2020 → 16 Oct 2020 Conference number: 5

Publication series

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

Conference

Conference	5th International Conference on NeuroRehabilitation, ICNR 2020
Abbreviated title	ICNR 2020
City	Virtual Event
Period	13/10/20 → 16/10/20

Keywords

22/1 OA procedure

Access to Document

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

Cavallo-2022-Identification-of-time-varying-anklFinal published version, 203 KBLicence: Taverne

Cite this

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.",

keywords = "22/1 OA procedure",

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

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Funding Information: This work was financially supported by the Research Foundation Flanders (FWO-Vlaanderen, Ph.D. fellowship nr 1182019N), by the Flemish Government (Methusalem Fund METH1) and by ERC Starting Grant INTERACT (803035). Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 5th International Conference on NeuroRehabilitation, ICNR 2020, ICNR 2020 ; 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, 5th International Conference on NeuroRehabilitation, ICNR 2020, Virtual Event, 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/Report/Conference proceeding › Chapter › Academic › 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 - Conference code: 5

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Y1 - 2022/10/2

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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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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