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

Wenlong Zhang; Xu Chen; Joonbum Bae; Masayoshi Tomizuka

doi:10.1109/ACC.2015.7172248

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 proceeding › Conference contribution

4 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 language	English (US)
Title of host publication	ACC 2015 - 2015 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5800-5805
Number of pages	6
ISBN (Electronic)	9781479986842
DOIs	https://doi.org/10.1109/ACC.2015.7172248
State	Published - Jul 28 2015
Externally published	Yes
Event	2015 American Control Conference, ACC 2015 - Chicago, United States Duration: Jul 1 2015 → Jul 3 2015

Publication series

Name	Proceedings of the American Control Conference
Volume	2015-July
ISSN (Print)	0743-1619

Other

Other	2015 American Control Conference, ACC 2015
Country/Territory	United States
City	Chicago
Period	7/1/15 → 7/3/15

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2015.7172248

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 ACC 2015 - 2015 American Control Conference (pp. 5800-5805). Article 7172248 (Proceedings of the American Control Conference; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7172248

Real-time kinematic modeling and prediction of human joint motion in a networked rehabilitation system. / Zhang, Wenlong; Chen, Xu; Bae, Joonbum et al.
ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5800-5805 7172248 (Proceedings of the American Control Conference; Vol. 2015-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 ACC 2015 - 2015 American Control Conference., 7172248, Proceedings of the American Control Conference, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 5800-5805, 2015 American Control Conference, ACC 2015, Chicago, United States, 7/1/15. https://doi.org/10.1109/ACC.2015.7172248

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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.",

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AB - 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.

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

Abstract

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