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Adaptive Estimation of Human-Robot Interaction Force for Lower Limb Rehabilitation

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Neural Information Processing (ICONIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1142))

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Abstract

Human-robot interaction force information is of great significance for realizing safe, compliant and efficient rehabilitation training. In order to accurately estimate the interaction force during human-robot interaction, an adaptive method for estimation of human-robot interaction force is proposed in this paper. Firstly, the dynamics of human-robot system are modeled, which allows to establish a state space equation. Then, the interaction force is described by a polynomial function of time, and is introduced into the state space equation as a system state. Meanwhile, the Kalman filter is adopted to estimate the extended state of system online. Moreover, in order to deal with the uncertainty of system noise covariance matrix, sage-husa adaptive Kalman filter is used to correct the covariance matrices of system noises online. Finally, experiments were carried out on a lower limb rehabilitation robot, and the results show that the proposed method can precisely estimate the interaction force and also has good real-time performance.

W. Wang—This research is supported in part by the National Key R&D Program of China (Grant 2017YFB1302303), National Natural Science Foundation of China (Grants 61720106012, 91848110), and Strategic Priority Research Program of Chinese Academy of Science (Grant XDB32000000).

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Authors and Affiliations

  1. Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Xu Liang, Weiqun Wang, Zengguang Hou, Shixin Ren, Jiaxing Wang & Weiguo Shi

  2. University of Chinese Academy of Sciences, Beijing, 100149, China

    Xu Liang, Zengguang Hou, Shixin Ren, Jiaxing Wang & Weiguo Shi

  3. CAS Center for Excellence in Brain Science and Intelligence Technology, Beijing, 100190, China

    Zengguang Hou

  4. North China University of Technology, Beijing, 100144, China

    Tingting Su

Authors
  1. Xu Liang
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  2. Weiqun Wang
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  3. Zengguang Hou
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  4. Shixin Ren
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  5. Jiaxing Wang
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  6. Weiguo Shi
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  7. Tingting Su
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Corresponding author

Correspondence to Weiqun Wang .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Liang, X. et al. (2019). Adaptive Estimation of Human-Robot Interaction Force for Lower Limb Rehabilitation. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_59

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  • DOI: https://doi.org/10.1007/978-3-030-36808-1_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36807-4

  • Online ISBN: 978-3-030-36808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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