A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals Article Swipe
Bangcheng Zhang
,
Xuteng Lan
,
Gang Wang
,
Zaixiang Pang
,
Xiyu Zhang
,
Zhongbo Sun
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnbot.2022.1047325
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnbot.2022.1047325
The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnbot.2022.1047325
- https://www.frontiersin.org/articles/10.3389/fnbot.2022.1047325/pdf
- OA Status
- gold
- Cited By
- 3
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308417218
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4308417218Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnbot.2022.1047325Digital Object Identifier
- Title
-
A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signalsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-03Full publication date if available
- Authors
-
Bangcheng Zhang, Xuteng Lan, Gang Wang, Zaixiang Pang, Xiyu Zhang, Zhongbo SunList of authors in order
- Landing page
-
https://doi.org/10.3389/fnbot.2022.1047325Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fnbot.2022.1047325/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.frontiersin.org/articles/10.3389/fnbot.2022.1047325/pdfDirect OA link when available
- Concepts
-
Computer science, Artificial neural network, Controller (irrigation), Robot, Artificial intelligence, Human–robot interaction, Noise (video), Rehabilitation, Rehabilitation robotics, Medicine, Physical therapy, Biology, Agronomy, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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