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Multi-class SVM Based Real-Time Recognition of Sit-to-Stand and Stand-to-Sit Transitions for a Bionic Knee Exoskeleton in Transparent Mode

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Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10462))

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Abstract

Real-time locomotion intent recognition is a challenge in lower-limb exoskeletons. In this paper, we present a multi-sensor based locomotion intent prediction system for sit-to-stand and stand-to-sit transition recognition the subject wears a knee exoskeleton in transparent mode. The desired reference torque for movement control is obtained from a direct torque control loop. The feedback torque is estimated by an inner current control loop. Five able-bodied subjects were recruited in experiments. The classifier is based on multi-class Support Vector Machine. Four kinds of modes and four kinds of transitions are tested in this study. Recognition accuracy during steady periods is 99.68% ± 0.07% for five able-bodied subjects. And during transition periods, all the transitions are correctly detected and no missed detections was observed for all the trials of the five subjects. Preliminary experimental results show that the proposed method is capable of performing real-time intent recognition and consequently reduces the interaction force between human body and the exoskeleton.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 91648207), the Beijing Municipal Science and Technology Project (No. Z151100000915073), and the Beijing Nova Program (No. Z141101001814001).

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Authors and Affiliations

  1. The Robotics Research Group, College of Engineering, Peking University, Beijing, 100871, China

    Xiuhua Liu, Zhihao Zhou, Jingeng Mai & Qining Wang

  2. Beijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT), Peking University, Beijing, 100871, China

    Qining Wang

Authors
  1. Xiuhua Liu
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  2. Zhihao Zhou
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  3. Jingeng Mai
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  4. Qining Wang
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Corresponding author

Correspondence to Qining Wang .

Editor information

Editors and Affiliations

  1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    YongAn Huang

  2. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Hao Wu

  3. Institute of Industrial Research, University of Portsmouth, Portsmouth, United Kingdom

    Honghai Liu

  4. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

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Liu, X., Zhou, Z., Mai, J., Wang, Q. (2017). Multi-class SVM Based Real-Time Recognition of Sit-to-Stand and Stand-to-Sit Transitions for a Bionic Knee Exoskeleton in Transparent Mode. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10462. Springer, Cham. https://doi.org/10.1007/978-3-319-65289-4_25

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  • DOI: https://doi.org/10.1007/978-3-319-65289-4_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65288-7

  • Online ISBN: 978-3-319-65289-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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