Light-Weight CNN-Attention Based Architecture Trained with a Hybrid Objective Function for EMG-Based Human Machine Interfaces
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Publisher
Springer Berlin Heidelberg
Link
https://link.springer.com/content/pdf/10.1007/978-3-662-67868-8_4
Reference43 articles.
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2. Rahimian, E., Zabihi, S., Asif, A., Atashzar, S.F., Mohammadi, A.: Few-shot learning for decoding surface electromyography for hand gesture recognition. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1300–1304 (2021)
3. Rahimian, E., Zabihi, S., Atashzar, F., Asif, A., Mohammadi, A.: XceptionTime: independent time-window XceptionTime architecture for hand gesture classification. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1304–1308 (2020)
4. Tsinganos, P., Cornelis, B., Cornelis, J., Jansen, B., Skodras, A.: Improved gesture recognition based on sEMG signals and TCN. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 1169–1173 (2019)
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