Time series prediction of knee joint movement and its application to a network-based rehabilitation system

Wenlong Zhang; Masayoshi Tomizuka; Joonbum Bae

doi:10.1109/ACC.2014.6859402

Time series prediction of knee joint movement and its application to a network-based rehabilitation system

Wenlong Zhang, Masayoshi Tomizuka, Joonbum Bae

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

11 Scopus citations

Abstract

In this paper, a network-based rehabilitation system is introduced for improved mobility and tele-rehabilitation. Time series of knee joint rotation measurement is obtained using the rehabilitation device in the system, and an autoregressive integrated (ARI) model is built to achieve knee joint angle prediction during the rehabilitation process. It is shown that the predicted knee joint angles are reliable over 10 future time steps. The ARI model and the predicted knee joint angles can provide insight to patients and therapists for deep understanding of patients' walking behaviors. Moreover, it is shown in this paper that the predicted knee joint angles can also be used to compensate for time delay and packet loss in the networked rehabilitation system to achieve accurate torque tracking. Simulation and experimental results are provided to demonstrate the performance of the proposed algorithm.

Original language	English (US)
Title of host publication	2014 American Control Conference, ACC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4810-4815
Number of pages	6
ISBN (Print)	9781479932726
DOIs	https://doi.org/10.1109/ACC.2014.6859402
State	Published - Jan 1 2014
Externally published	Yes
Event	2014 American Control Conference, ACC 2014 - Portland, OR, United States Duration: Jun 4 2014 → Jun 6 2014

Publication series

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

Other

Other	2014 American Control Conference, ACC 2014
Country/Territory	United States
City	Portland, OR
Period	6/4/14 → 6/6/14

Keywords

Biomedical
Networked control systems
Statistical learning

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2014.6859402

Cite this

Zhang, W., Tomizuka, M., & Bae, J. (2014). Time series prediction of knee joint movement and its application to a network-based rehabilitation system. In 2014 American Control Conference, ACC 2014 (pp. 4810-4815). Article 6859402 (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2014.6859402

Time series prediction of knee joint movement and its application to a network-based rehabilitation system. / Zhang, Wenlong; Tomizuka, Masayoshi; Bae, Joonbum.
2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 4810-4815 6859402 (Proceedings of the American Control Conference).

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

Zhang, W, Tomizuka, M & Bae, J 2014, Time series prediction of knee joint movement and its application to a network-based rehabilitation system. in 2014 American Control Conference, ACC 2014., 6859402, Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers Inc., pp. 4810-4815, 2014 American Control Conference, ACC 2014, Portland, OR, United States, 6/4/14. https://doi.org/10.1109/ACC.2014.6859402

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abstract = "In this paper, a network-based rehabilitation system is introduced for improved mobility and tele-rehabilitation. Time series of knee joint rotation measurement is obtained using the rehabilitation device in the system, and an autoregressive integrated (ARI) model is built to achieve knee joint angle prediction during the rehabilitation process. It is shown that the predicted knee joint angles are reliable over 10 future time steps. The ARI model and the predicted knee joint angles can provide insight to patients and therapists for deep understanding of patients' walking behaviors. Moreover, it is shown in this paper that the predicted knee joint angles can also be used to compensate for time delay and packet loss in the networked rehabilitation system to achieve accurate torque tracking. Simulation and experimental results are provided to demonstrate the performance of the proposed algorithm.",

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N2 - In this paper, a network-based rehabilitation system is introduced for improved mobility and tele-rehabilitation. Time series of knee joint rotation measurement is obtained using the rehabilitation device in the system, and an autoregressive integrated (ARI) model is built to achieve knee joint angle prediction during the rehabilitation process. It is shown that the predicted knee joint angles are reliable over 10 future time steps. The ARI model and the predicted knee joint angles can provide insight to patients and therapists for deep understanding of patients' walking behaviors. Moreover, it is shown in this paper that the predicted knee joint angles can also be used to compensate for time delay and packet loss in the networked rehabilitation system to achieve accurate torque tracking. Simulation and experimental results are provided to demonstrate the performance of the proposed algorithm.

AB - In this paper, a network-based rehabilitation system is introduced for improved mobility and tele-rehabilitation. Time series of knee joint rotation measurement is obtained using the rehabilitation device in the system, and an autoregressive integrated (ARI) model is built to achieve knee joint angle prediction during the rehabilitation process. It is shown that the predicted knee joint angles are reliable over 10 future time steps. The ARI model and the predicted knee joint angles can provide insight to patients and therapists for deep understanding of patients' walking behaviors. Moreover, it is shown in this paper that the predicted knee joint angles can also be used to compensate for time delay and packet loss in the networked rehabilitation system to achieve accurate torque tracking. Simulation and experimental results are provided to demonstrate the performance of the proposed algorithm.

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KW - Statistical learning

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Time series prediction of knee joint movement and its application to a network-based rehabilitation system

Abstract

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Keywords

ASJC Scopus subject areas

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