Movement recognition via channel-activation-wise sEMG attention

Zhang, Jiaxuan; Matsuda, Yuki; Fujimoto, Manato; Suwa, Hirohiko; Yasumoto, Keiichi

doi:10.1016/j.ymeth.2023.06.011

Methods

2023

DOI: 10.1016/j.ymeth.2023.06.011

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Movement recognition via channel-activation-wise sEMG attention

Jiaxuan Zhang

Yuki Matsuda

Manato Fujimoto³

et al.

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Cited by 3 publications

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An end-to-end hand action recognition framework based on cross-time mechanomyography signals

Zhang,

Li,

Zhang

et al. 2024

Complex Intell. Syst.

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The susceptibility of mechanomyography (MMG) signals acquisition to sensor donning and doffing, and the apparent time-varying characteristics of biomedical signals collected over different periods, inevitably lead to a reduction in model recognition accuracy. To investigate the adverse effects on the recognition results of hand actions, a 12-day cross-time MMG data collection experiment with eight subjects was conducted by an armband, then a novel MMG-based hand action recognition framework with densely connected convolutional networks (DenseNet) was proposed. In this study, data from 10 days were selected as a training subset, and the remaining data from another 2 days were used as a test set to evaluate the model’s performance. As the number of days in the training set increases, the recognition accuracy increases and becomes more stable, peaking when the training set includes 10 days and achieving an average recognition rate of 99.57% (± 0.37%). In addition, part of the training subset is extracted and recombined into a new dataset and the better classification performances of models can be achieved from the test set. The method proposed effectively mitigates the adverse effects of sensor donning and doffing on recognition results.

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An end-to-end hand action recognition framework based on cross-time mechanomyography signals

Zhang,

Li,

Zhang

et al. 2024

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

Deep learning methods for bioinformatics and biomedicine

Wang

Zou

2023

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Deep learning methods in biomedical informatics

Zhang,

Tony Hu,

Cereda

et al. 2024

Methods

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Movement recognition via channel-activation-wise sEMG attention

Cited by 3 publications

References 38 publications

An end-to-end hand action recognition framework based on cross-time mechanomyography signals

An end-to-end hand action recognition framework based on cross-time mechanomyography signals

Deep learning methods for bioinformatics and biomedicine

Deep learning methods in biomedical informatics

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