Robust identification of multi-joint human arm impedance based on dynamics decomposition: a modeling study.

Sang Hoon Kang*, Li Qun Zhang

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Multi-joint/multi-degree of freedom (DOF) human arm impedance estimation is important in many disciplines. However, as the number of joints/DOFs increases, it may become intractable to identify the system reliably. A robust, unbiased and tractable estimation method based on a systematic dynamics decomposition, which decomposes a multi-input multi-output (MIMO) system into multiple single-input multi-output (SIMO) subsystems, is developed. Accuracy and robustness of the new method were validated through a human arm and a 2-DOF exoskeleton robot simulation with various magnitudes of sensor resolution and nonlinear friction. The approach can be similarly applied to identify more sophisticated systems with more joints/DOFs involved.

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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