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Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control
Joint Authors
Sabourin, Cristophe
Yu, Weiwei
Ma, Weihua
Feng, Yangyang
Wang, Runxiao
Madani, Kurosh
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-29
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
In the case of dynamic motion such as jumping, an important fact in sEMG (surface Electromyogram) signal based control on exoskeletons, myoelectric prostheses, and rehabilitation gait is that multichannel sEMG signals contain mass data and vary greatly with time, which makes it difficult to generate compliant gait.
Inspired by the fact that muscle synergies leading to dimensionality reduction may simplify motor control and learning, this paper proposes a new approach to generate flexible gait based on muscle synergies extracted from sEMG signal.
Two questions were discussed and solved, the first one concerning whether the same set of muscle synergies can explain the different phases of hopping movement with various velocities.
The second one is about how to generate self-adapted gait with muscle synergies while alleviating model sensitivity to sEMG transient changes.
From the experimental results, the proposed method shows good performance both in accuracy and in robustness for producing velocity-adapted vertical jumping gait.
The method discussed in this paper provides a valuable reference for the sEMG-based control of bionic robot leg to generate human-like dynamic gait.
American Psychological Association (APA)
Yu, Weiwei& Ma, Weihua& Feng, Yangyang& Wang, Runxiao& Madani, Kurosh& Sabourin, Cristophe. 2017. Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control. Journal of Sensors،Vol. 2017, no. 2017, pp.1-18.
https://search.emarefa.net/detail/BIM-1187332
Modern Language Association (MLA)
Yu, Weiwei…[et al.]. Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control. Journal of Sensors No. 2017 (2017), pp.1-18.
https://search.emarefa.net/detail/BIM-1187332
American Medical Association (AMA)
Yu, Weiwei& Ma, Weihua& Feng, Yangyang& Wang, Runxiao& Madani, Kurosh& Sabourin, Cristophe. Generating Human-Like Velocity-Adapted Jumping Gait from sEMG Signals for Bionic Leg’s Control. Journal of Sensors. 2017. Vol. 2017, no. 2017, pp.1-18.
https://search.emarefa.net/detail/BIM-1187332
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187332