Online Unsupervised Adaptation of Latent Representation for Myoelectric Control During User-Decoder Co-Adaptation

Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which m...

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Vydané v:	IEEE transactions on neural systems and rehabilitation engineering Ročník 33; s. 1026 - 1037
Hlavní autori:	Deng, Hanjie, Wei, Zhikai, Hu, Xuhui, Zeng, Hong, Song, Aiguo, Zhang, Dingguo, Farina, Dario
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 2025
Predmet:	Adult Algorithms Artificial Limbs Autoencoders Calibration decoder adaptation Decoding electrode shift Electrodes Electromyography Electromyography - methods Female Humans Male Manifolds Muscle, Skeletal - physiology Myoelectric control online manifold learning Online Systems Perturbation methods Reproducibility of Results Robot sensing systems Robustness Training unsupervised autoencoder Unsupervised Machine Learning User-Computer Interface
ISSN:	1534-4320, 1558-0210, 1558-0210
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Abstract	Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation.
AbstractList	Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation.Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation. Myoelectric control interfaces, which map electromyographic (EMG) signals into control commands for external devices, have applications in active prosthesis control. However, the statistical characteristics of EMG signals change over time (e.g., because of changes in the electrode location), which makes interfaces based on static mapping unstable. Thus the user-decoder co-adaptation is needed during online operations. Nevertheless, current online decoder adaptation approaches present several practical challenges, such as expensive data labeling and slow convergence. Thus we introduce an unsupervised decoder adaptation method that converges rapidly. We use an autoencoder to extract motor intent representation in the latent manifold space rather than the sensor space, and further introduce an online unsupervised adaptation scheme based on Moore-Penrose Inverse, a noniterative approach suited for fast network re-training, to track the evolving manifold. A validation experiment first showed that the convergence time of the proposed adaptation scheme was reduced to about 50% of that for state-of-the-art methods. Online experiments further evaluated cursor and prosthetic hand control by the proposed myocontrol interface, where perturbations were representatively introduced by shifting the electrodes. Results showed that our scheme reached comparable improvements in robustness as supervised counterparts. Moreover, in a cup relocation test with a prosthetic hand, the completion time in the post-adaptation phase with electrode shift was comparable to that in the baseline phase without shift. These results suggest that our method effectively improves the accessibility and reliability of decoder adaptation, which has the potential to reduce the translational gap of myoelectric control interfaces by effective co-adaptation during operation.
Author	Song, Aiguo Zhang, Dingguo Zeng, Hong Wei, Zhikai Hu, Xuhui Deng, Hanjie Farina, Dario
Author_xml	– sequence: 1 givenname: Hanjie surname: Deng fullname: Deng, Hanjie organization: State Key Laboratory of Digital Medical Engineering, Jiangsu Province Key Laboratory of Robot Sensing and Control, and the School of Instrument Science and Engineering, Southeast University, Nanjing, China – sequence: 2 givenname: Zhikai orcidid: 0009-0001-6820-9164 surname: Wei fullname: Wei, Zhikai organization: State Key Laboratory of Digital Medical Engineering, Jiangsu Province Key Laboratory of Robot Sensing and Control, and the School of Instrument Science and Engineering, Southeast University, Nanjing, China – sequence: 3 givenname: Xuhui orcidid: 0000-0001-7632-3090 surname: Hu fullname: Hu, Xuhui organization: Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China – sequence: 4 givenname: Hong orcidid: 0000-0002-4587-6263 surname: Zeng fullname: Zeng, Hong email: hzeng@seu.edu.cn organization: State Key Laboratory of Digital Medical Engineering, Jiangsu Province Key Laboratory of Robot Sensing and Control, and the School of Instrument Science and Engineering, Southeast University, Nanjing, China – sequence: 5 givenname: Aiguo orcidid: 0000-0002-1982-6780 surname: Song fullname: Song, Aiguo organization: State Key Laboratory of Digital Medical Engineering, Jiangsu Province Key Laboratory of Robot Sensing and Control, and the School of Instrument Science and Engineering, Southeast University, Nanjing, China – sequence: 6 givenname: Dingguo surname: Zhang fullname: Zhang, Dingguo organization: Department of Electronic and Electrical Engineering, University of Bath, Bath, U.K – sequence: 7 givenname: Dario orcidid: 0000-0002-7883-2697 surname: Farina fullname: Farina, Dario organization: Department of Bioengineering, Imperial College London, London, U.K
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SubjectTerms	Adult Algorithms Artificial Limbs Autoencoders Calibration decoder adaptation Decoding electrode shift Electrodes Electromyography Electromyography - methods Female Humans Male Manifolds Muscle, Skeletal - physiology Myoelectric control online manifold learning Online Systems Perturbation methods Reproducibility of Results Robot sensing systems Robustness Training unsupervised autoencoder Unsupervised Machine Learning User-Computer Interface
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Title	Online Unsupervised Adaptation of Latent Representation for Myoelectric Control During User-Decoder Co-Adaptation
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