Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric Interfaces
<inline-formula> <tex-math notation="LaTeX">$\textit {Objective:}$ </tex-math></inline-formula> This study aims to develop a flexible myoelectric pattern recognition (MPR) method based on one-shot learning, which enables convenient switching across different usage s...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10061471/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849734398114529280 |
|---|---|
| author | Xiang Wang Xu Zhang Xiang Chen Xun Chen Zhao Lv Zhen Liang |
| author_facet | Xiang Wang Xu Zhang Xiang Chen Xun Chen Zhao Lv Zhen Liang |
| author_sort | Xiang Wang |
| collection | DOAJ |
| description | <inline-formula> <tex-math notation="LaTeX">$\textit {Objective:}$ </tex-math></inline-formula> This study aims to develop a flexible myoelectric pattern recognition (MPR) method based on one-shot learning, which enables convenient switching across different usage scenarios, thereby reducing the re-training burden. <inline-formula> <tex-math notation="LaTeX">$\textit {Methods}$ </tex-math></inline-formula>: First, a one-shot learning model based on a Siamese neural network was constructed to assess the similarity for any given sample pair. In a new scenario involving a new set of gestural categories and/or a new user, just one sample of each category was required to constitute a support set. This enabled the quick deployment of the classifier suitable for the new scenario, which decided for any unknown query sample by selecting the category whose sample in the support set was quantified to be the most like the query sample. The effectiveness of the proposed method was evaluated by experiments conducting MPR across diverse scenarios. Results: The proposed method achieved high recognition accuracy of over 89% under the cross-scenario conditions, and it significantly outperformed other common one-shot learning methods and conventional MPR methods (<inline-formula> <tex-math notation="LaTeX">${p} < 0.01$ </tex-math></inline-formula>). <inline-formula> <tex-math notation="LaTeX">$\textit {Conclusion}$ </tex-math></inline-formula>: This study demonstrates the feasibility of applying one-shot learning to rapidly deploy myoelectric pattern classifiers in response to scenario change. It provides a valuable way of improving the flexibility of myoelectric interfaces toward intelligent gestural control with extensive applications in medical, industrial, and consumer electronics. |
| format | Article |
| id | doaj-art-61e4fd7ce92e4ce1b2a3dc0e1c0490d7 |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-61e4fd7ce92e4ce1b2a3dc0e1c0490d72025-08-20T03:07:47ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-01311697170610.1109/TNSRE.2023.325368310061471Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric InterfacesXiang Wang0https://orcid.org/0009-0009-6969-7508Xu Zhang1https://orcid.org/0000-0002-1533-4340Xiang Chen2https://orcid.org/0000-0001-8259-4815Xun Chen3https://orcid.org/0000-0002-4922-8116Zhao Lv4https://orcid.org/0000-0001-9727-366XZhen Liang5Department of Biomedical Engineering, Anhui Medical University, Hefei, ChinaSchool of Microelectronics, University of Science and Technology of China, Hefei, ChinaSchool of Microelectronics, University of Science and Technology of China, Hefei, ChinaSchool of Microelectronics, University of Science and Technology of China, Hefei, ChinaInstitute of Physical Science and Information Technology, Anhui University, Hefei, ChinaDepartment of Biomedical Engineering, Anhui Medical University, Hefei, China<inline-formula> <tex-math notation="LaTeX">$\textit {Objective:}$ </tex-math></inline-formula> This study aims to develop a flexible myoelectric pattern recognition (MPR) method based on one-shot learning, which enables convenient switching across different usage scenarios, thereby reducing the re-training burden. <inline-formula> <tex-math notation="LaTeX">$\textit {Methods}$ </tex-math></inline-formula>: First, a one-shot learning model based on a Siamese neural network was constructed to assess the similarity for any given sample pair. In a new scenario involving a new set of gestural categories and/or a new user, just one sample of each category was required to constitute a support set. This enabled the quick deployment of the classifier suitable for the new scenario, which decided for any unknown query sample by selecting the category whose sample in the support set was quantified to be the most like the query sample. The effectiveness of the proposed method was evaluated by experiments conducting MPR across diverse scenarios. Results: The proposed method achieved high recognition accuracy of over 89% under the cross-scenario conditions, and it significantly outperformed other common one-shot learning methods and conventional MPR methods (<inline-formula> <tex-math notation="LaTeX">${p} < 0.01$ </tex-math></inline-formula>). <inline-formula> <tex-math notation="LaTeX">$\textit {Conclusion}$ </tex-math></inline-formula>: This study demonstrates the feasibility of applying one-shot learning to rapidly deploy myoelectric pattern classifiers in response to scenario change. It provides a valuable way of improving the flexibility of myoelectric interfaces toward intelligent gestural control with extensive applications in medical, industrial, and consumer electronics.https://ieeexplore.ieee.org/document/10061471/Myoelectric controlelectromyogram (EMG)one-shot learningcross-scenarioflexibility |
| spellingShingle | Xiang Wang Xu Zhang Xiang Chen Xun Chen Zhao Lv Zhen Liang Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric Interfaces IEEE Transactions on Neural Systems and Rehabilitation Engineering Myoelectric control electromyogram (EMG) one-shot learning cross-scenario flexibility |
| title | Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric Interfaces |
| title_full | Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric Interfaces |
| title_fullStr | Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric Interfaces |
| title_full_unstemmed | Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric Interfaces |
| title_short | Similarity Function for One-Shot Learning to Enhance the Flexibility of Myoelectric Interfaces |
| title_sort | similarity function for one shot learning to enhance the flexibility of myoelectric interfaces |
| topic | Myoelectric control electromyogram (EMG) one-shot learning cross-scenario flexibility |
| url | https://ieeexplore.ieee.org/document/10061471/ |
| work_keys_str_mv | AT xiangwang similarityfunctionforoneshotlearningtoenhancetheflexibilityofmyoelectricinterfaces AT xuzhang similarityfunctionforoneshotlearningtoenhancetheflexibilityofmyoelectricinterfaces AT xiangchen similarityfunctionforoneshotlearningtoenhancetheflexibilityofmyoelectricinterfaces AT xunchen similarityfunctionforoneshotlearningtoenhancetheflexibilityofmyoelectricinterfaces AT zhaolv similarityfunctionforoneshotlearningtoenhancetheflexibilityofmyoelectricinterfaces AT zhenliang similarityfunctionforoneshotlearningtoenhancetheflexibilityofmyoelectricinterfaces |