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- W4285338032 abstract "Hand movement recognition plays an important role in the emerging technologies of rehabilitation engineering and human-computer interaction. Among various hand movement recognition techniques, muscle-computer interface (MCI) has drawn great attention from researchers due to its wearability, insensitivity to self-occlusion and illumination condition, and capability of distinguishing subtle hand movements. Based on the theory that dominant muscle groups play more important roles in certain hand movements, this paper proposed a multi-stream locally-connected neural network (MSLCNet) framework, which aims at performing independent deep learning at different locations of the forearm to better model human muscle activities from the potential dominant muscle groups during certain hand movements. Experimental results showed that the proposed MSLCNet could achieved the hand movement recognition accuracies of 87.7%, 84.5% and 70.4% on three different sEMG datasets, which outperformed the state-of-the-art methods." @default.
- W4285338032 created "2022-07-14" @default.
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- W4285338032 date "2021-11-01" @default.
- W4285338032 modified "2023-09-26" @default.
- W4285338032 title "MSLCNet: A Multi-Stream Locally-Connected Neural Network For Surface Electromyography based Hand Movement Recognition" @default.
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- W4285338032 doi "https://doi.org/10.1109/icaice54393.2021.00072" @default.
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