Journal
SENSORS
Volume 18, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/s18103226
Keywords
force estimation; surface electromyography; neural network
Funding
- National Key Research and Development Program of China [2017YFB1300301]
- National Natural Science Foundation of China [61671417]
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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.
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