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sEMG Based Human Motion Intention Recognition
Joint Authors
Liu, Geng
Zhang, Li
Han, Bing
Wang, Zhe
Zhang, Tong
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-08-05
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots.
Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly.
Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition.
In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail.
According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition.
The specific models and recognition effects of each study are analyzed and systematically compared.
Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.
American Psychological Association (APA)
Zhang, Li& Liu, Geng& Han, Bing& Wang, Zhe& Zhang, Tong. 2019. sEMG Based Human Motion Intention Recognition. Journal of Robotics،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1186943
Modern Language Association (MLA)
Zhang, Li…[et al.]. sEMG Based Human Motion Intention Recognition. Journal of Robotics No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1186943
American Medical Association (AMA)
Zhang, Li& Liu, Geng& Han, Bing& Wang, Zhe& Zhang, Tong. sEMG Based Human Motion Intention Recognition. Journal of Robotics. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1186943
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186943