Real-time kinematic modeling and prediction of human joint motion in a networked rehabilitation system

Wenlong Zhang, Xu Chen, Joonbum Bae, Masayoshi Tomizuka

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, a networked-based rehabilitation system is introduced for lower-extremity tele-rehabilitation. In order to enable high-level motion planning of the rehabilitation robot in real-time for enhanced safety and appropriate human-robot interactions, a time series model is proposed to capture the kinematics of knee joint rotations. A major challenge in such a system is that measurement data might be delayed or lost due to wireless communication. With a delay and loss compensation mechanism, a modified recursive least square (mRLS) algorithm is applied for real-time modeling and prediction of knee joint rotations in the sagittal plane, and convergence of the proposed algorithm is studied. Simulation and experimental results are presented to verify the performance of the proposed algorithm.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5800-5805
Number of pages6
Volume2015-July
ISBN (Print)9781479986842
DOIs
StatePublished - Jul 28 2015
Externally publishedYes
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Other

Other2015 American Control Conference, ACC 2015
CountryUnited States
CityChicago
Period7/1/157/3/15

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Zhang, W., Chen, X., Bae, J., & Tomizuka, M. (2015). Real-time kinematic modeling and prediction of human joint motion in a networked rehabilitation system. In Proceedings of the American Control Conference (Vol. 2015-July, pp. 5800-5805). [7172248] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7172248