Real-Time On-Board Recognition of Locomotion Modes for an Active Pelvis Orthosis

Cheng Gong, Dongfang Xu, Zhihao Zhou, Nicola Vitiello, Qining Wang

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

Abstract

To adapt to different locomotion modes or terrains, real-time human intents recognition is an essential skill to the control of lower-limb exoskeletons timely and precisely. In this paper, we propose a real-time on-board training and recognition method to identify locomotion-related activities for an active pelvis orthosis using two IMUs integrated into it. The designed on-board intent recognition system with a BPNN based algorithm realizes distinguish among six locomotion modes including standing, level ground walking, ramp ascending, ramp descending, stair ascending and stair descending, and deliver the recognition results for future control strategies. Experiments are conducted on one healthy subject including on-board training and online recognition parts. The overall recognition accuracy is 97.79% with the cost time of one recognition decision is about 0.9ms, which is sufficient short compared with the sample interval of 10ms. The experimental results validate the great performance of the proposed real-time on-board training and recognition method for future control of the lower-limb exoskeletons assisting in various locomotion modes or terrains.

Original languageEnglish
Title of host publication2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018
PublisherIEEE Computer Society
Pages346-350
Number of pages5
ISBN (Electronic)9781538672839
DOIs
Publication statusPublished - Jan 23 2019
Event18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018 - Beijing, China
Duration: Nov 6 2018Nov 9 2018

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
Volume2018-November
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580

Conference

Conference18th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2018
CountryChina
CityBeijing
Period11/6/1811/9/18

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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  • Cite this

    Gong, C., Xu, D., Zhou, Z., Vitiello, N., & Wang, Q. (2019). Real-Time On-Board Recognition of Locomotion Modes for an Active Pelvis Orthosis. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots, Humanoids 2018 (pp. 346-350). [8625044] (IEEE-RAS International Conference on Humanoid Robots; Vol. 2018-November). IEEE Computer Society. https://doi.org/10.1109/HUMANOIDS.2018.8625044