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

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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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 languageEnglish
Title of host publicationConverging Clinical and Engineering Research on Neurorehabilitation IV
Subtitle of host publicationProceedings of the 5th International Conference on Neurorehabilitation (ICNR2020), October 13–16, 2020
EditorsDiego Torricelli, Metin Akay, Jose L. Pons
Number of pages6
ISBN (Electronic)978-3-030-70316-5
ISBN (Print)978-3-030-70315-8, 978-3-030-70318-9
Publication statusPublished - 2022
Externally publishedYes
Event5th International Conference on NeuroRehabilitation, ICNR 2020 - Virtual Event
Duration: 13 Oct 202016 Oct 2020
Conference number: 5

Publication series

NameBiosystems and Biorobotics
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570


Conference5th International Conference on NeuroRehabilitation, ICNR 2020
Abbreviated titleICNR 2020
CityVirtual Event


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