Quantitative comparison of time-varying system identification methods to describe human joint impedance

Mark van de Ruit*, Winfred Mugge, Gaia Cavallo, John Lataire, Daniel Ludvig, Alfred C. Schouten

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Accurate and swift tuning of joint impedance is crucial to perform movement and interaction with our environment. Time-varying system identification enables quantification of joint impedance during movement. Many methods have been developed over the years, each with their own mathematical approach and underlying assumptions. Yet, for the identification of joint impedance, a systematic comparison revealing each method's unique strengths and weaknesses, is lacking. Here, we propose a quantitative framework to compare these methods. The framework is used to review five time-varying system identification methods using both simulated data and experimental data. These methods included three time-domain methods: ensemble, short data segment, and basis impulse response function; and two frequency-domain methods: ensemble spectral, and kernel-based regression. In the simulation study, joint stiffness – the static component of impedance – was simulated as a square wave to mimic the most extreme case of time-varying behavior. The identification results were compared based on the (1) variance accounted for (VAF), (2) bias, (3) random, and (4) total estimation error with respect to the simulated joint stiffness; and (5) rise time between two stiffness levels. In the experimental study, human ankle joint impedance was identified. Identification performance was compared using the variability in estimating joint stiffness – representative of the random error – and VAF. The performance metrics revealed distinct identification properties for each method. Therefore, researchers must make a well-justified decision which method is most appropriate for their application. The combination of simulation and experimental work with extensive performance quantification creates a framework for quantitative assessment of newly developed time-varying system identification methods.

Original languageEnglish (US)
Pages (from-to)91-107
Number of pages17
JournalAnnual Reviews in Control
Volume52
DOIs
StatePublished - Jan 2021

Funding

He is currently a postdoctoral researcher at Delft University of Technology, the Netherlands. His-research interest is in neuromuscular control, combing electrophysiological recording techniques with system identification to obtain a better understanding of human motion control. In 2020 he received a prestigious VENI grant from the Dutch Research Council (NWO) to fund his research project investigating reflex modulation during movement.

Keywords

  • Human motor control
  • Joint impedance
  • System identification
  • Time-varying systems

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering

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