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• W2892959507 abstract "To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 ± 1.29 and 8.67 ± 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 ± 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation." @default.
• W2892959507 created "2018-10-05" @default.
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• W2892959507 date "2018-09-25" @default.
• W2892959507 modified "2023-10-10" @default.
• W2892959507 title "Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation" @default.
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• W2892959507 doi "https://doi.org/10.3390/s18103226" @default.
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