A Muscle Model Incorporating Fiber Architecture Features for the Estimation of Joint Stiffness During Dynamic Movement

Christopher P. Cop; Alfred C. Schouten; Bart F.J.M. Koopman; M. Sartori

doi:10.1007/978-3-030-70316-5_81

A Muscle Model Incorporating Fiber Architecture Features for the Estimation of Joint Stiffness During Dynamic Movement

Christopher P. Cop^*, Alfred C. Schouten, Bart F.J.M. Koopman, M. Sartori

^*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

Quantifying human joint stiffness in vivo during movement remains challenging. Well established stiffness estimation methods include system identification and the notion of quasi-stiffness, with experimental and conceptual limitations, respectively. Joint stiffness computation via biomechanical models is an emerging solution to overcome such limitations. However, these models make assumptions that hamper their generalization across muscle architectures. Here we present a stiffness formulation that considers the muscle’s pennation angle, and its comparison to a simpler formulation that does not. Model-based stiffness estimates are evaluated against joint-perturbation-based system identification. Results on muscles with different pennation angle show that our formulation seamlessly adjusts the muscle-tendon units’ stiffness depending on their architecture. At the joint level, our new model improved the stiffness estimations. Our study’s relevance is the creation and validation of a modeling formulation that does not require joint perturbation. This will enable better estimations and understanding of stiffness properties and human movement.

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	507-511
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_81
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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Cop2022_Chapter_AMuscleModelIncorporatingFiberFinal published version, 165 KB

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Cop, C. P., Schouten, A. C., Koopman, B. F. J. M., & Sartori, M. (2022). A Muscle Model Incorporating Fiber Architecture Features for the Estimation of Joint Stiffness During Dynamic Movement. 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. 507-511). (Biosystems and Biorobotics; Vol. 28). Springer. https://doi.org/10.1007/978-3-030-70316-5_81

Cop, Christopher P. ; Schouten, Alfred C. ; Koopman, Bart F.J.M. et al. / A Muscle Model Incorporating Fiber Architecture Features for the Estimation of Joint Stiffness During Dynamic Movement. 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. 507-511 (Biosystems and Biorobotics).

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abstract = "Quantifying human joint stiffness in vivo during movement remains challenging. Well established stiffness estimation methods include system identification and the notion of quasi-stiffness, with experimental and conceptual limitations, respectively. Joint stiffness computation via biomechanical models is an emerging solution to overcome such limitations. However, these models make assumptions that hamper their generalization across muscle architectures. Here we present a stiffness formulation that considers the muscle{\textquoteright}s pennation angle, and its comparison to a simpler formulation that does not. Model-based stiffness estimates are evaluated against joint-perturbation-based system identification. Results on muscles with different pennation angle show that our formulation seamlessly adjusts the muscle-tendon units{\textquoteright} stiffness depending on their architecture. At the joint level, our new model improved the stiffness estimations. Our study{\textquoteright}s relevance is the creation and validation of a modeling formulation that does not require joint perturbation. This will enable better estimations and understanding of stiffness properties and human movement.",

author = "Cop, {Christopher P.} and Schouten, {Alfred C.} and Koopman, {Bart F.J.M.} and M. Sartori",

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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Cop, CP, Schouten, AC, Koopman, BFJM & Sartori, M 2022, A Muscle Model Incorporating Fiber Architecture Features for the Estimation of Joint Stiffness During Dynamic Movement. 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. 507-511, ICNR 2020: International Conference on NeuroRehabilitation (Virtual), 13/10/20. https://doi.org/10.1007/978-3-030-70316-5_81

A Muscle Model Incorporating Fiber Architecture Features for the Estimation of Joint Stiffness During Dynamic Movement. / Cop, Christopher P.; Schouten, Alfred C.; Koopman, Bart F.J.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. 507-511 (Biosystems and Biorobotics; Vol. 28).

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

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T1 - A Muscle Model Incorporating Fiber Architecture Features for the Estimation of Joint Stiffness During Dynamic Movement

AU - Cop, Christopher P.

AU - Schouten, Alfred C.

AU - Koopman, Bart F.J.M.

AU - Sartori, M.

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 - Quantifying human joint stiffness in vivo during movement remains challenging. Well established stiffness estimation methods include system identification and the notion of quasi-stiffness, with experimental and conceptual limitations, respectively. Joint stiffness computation via biomechanical models is an emerging solution to overcome such limitations. However, these models make assumptions that hamper their generalization across muscle architectures. Here we present a stiffness formulation that considers the muscle’s pennation angle, and its comparison to a simpler formulation that does not. Model-based stiffness estimates are evaluated against joint-perturbation-based system identification. Results on muscles with different pennation angle show that our formulation seamlessly adjusts the muscle-tendon units’ stiffness depending on their architecture. At the joint level, our new model improved the stiffness estimations. Our study’s relevance is the creation and validation of a modeling formulation that does not require joint perturbation. This will enable better estimations and understanding of stiffness properties and human movement.

AB - Quantifying human joint stiffness in vivo during movement remains challenging. Well established stiffness estimation methods include system identification and the notion of quasi-stiffness, with experimental and conceptual limitations, respectively. Joint stiffness computation via biomechanical models is an emerging solution to overcome such limitations. However, these models make assumptions that hamper their generalization across muscle architectures. Here we present a stiffness formulation that considers the muscle’s pennation angle, and its comparison to a simpler formulation that does not. Model-based stiffness estimates are evaluated against joint-perturbation-based system identification. Results on muscles with different pennation angle show that our formulation seamlessly adjusts the muscle-tendon units’ stiffness depending on their architecture. At the joint level, our new model improved the stiffness estimations. Our study’s relevance is the creation and validation of a modeling formulation that does not require joint perturbation. This will enable better estimations and understanding of stiffness properties and human movement.

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DO - 10.1007/978-3-030-70316-5_81

M3 - Chapter

AN - SCOPUS:85116882495

SN - 978-3-030-70315-8

T3 - Biosystems and Biorobotics

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EP - 511

BT - Converging Clinical and Engineering Research on Neurorehabilitation IV

A2 - Torricelli, Diego

A2 - Akay, Metin

A2 - Pons, Jose L.

PB - Springer

T2 - ICNR 2020: International Conference on NeuroRehabilitation (Virtual)

Y2 - 13 October 2020 through 16 October 2020

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Cop CP, Schouten AC, Koopman BFJM, Sartori M. A Muscle Model Incorporating Fiber Architecture Features for the Estimation of Joint Stiffness During Dynamic Movement. 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. 507-511. (Biosystems and Biorobotics). doi: 10.1007/978-3-030-70316-5_81

A Muscle Model Incorporating Fiber Architecture Features for the Estimation of Joint Stiffness During Dynamic Movement

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