ArticleSensors (Basel, Switzerland)2022
CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Few-shot prototype adaptation for generalizable electromyography gesture recognition.Scientific reports · 2026Article
- Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications.National science review · 2024Review
- A difficulty-aware and task-augmentation method based on meta-learning model for few-shot diabetic retinopathy classification.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human-computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to develop a model that can quickly adapt to new subjects. In view of this, we introduce a new deep neural network called CSAC-Net. Firstly, we extract the time-frequency feature from the raw signal, which contains rich information. Secondly, we design a convolutional neural network supplemented by an attention mechanism for further feature extraction. Additionally, we propose to utilize model-agnostic meta-learning to adapt to new subjects and this learning strategy achieves better results than the state-of-the-art methods. By the basic experiment on CapgMyo and three ablation studies, we demonstrate the advancement of CSAC-Net.
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Registered trials
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