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sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization
Joint Authors
Ma, Fujun
Song, Fanghao
Niu, Jiahui
Liu, Yan
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-11
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
The fatigue energy consumption of independent gestures can be obtained by calculating the power spectrum of surface electromyography (sEMG) signals.
The existing research studies focus on the fatigue of independent gestures, while the research studies on integrated gestures are few.
However, the actual gesture operation mode is usually integrated by multiple independent gestures, so the fatigue degree of integrated gestures can be predicted by training neural network of independent gestures.
Three natural gestures including browsing information, playing games, and typing are divided into nine independent gestures in this paper, and the predicted model is established and trained by calculating the energy consumption of independent gestures.
The artificial neural networks (ANNs) including backpropagation (BP) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) are used to predict the fatigue of gesture.
The support vector machine (SVM) is used to assist verification.
Mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are utilized to evaluate the optimal prediction model.
Furthermore, the different datasets of the processed sEMG signal and its decomposed wavelet coefficients are trained, respectively, and the changes of error functions of them are compared.
The experimental results show that LSTM model is more suitable for gesture fatigue prediction.
The processed sEMG signals are appropriate for using as the training set the fatigue degree of one-handed gesture.
It is better to use wavelet decomposition coefficients as datasets to predict the high-dimensional sEMG signals of two-handed gestures.
The experimental results can be applied to predict the fatigue degree of complex human-machine interactive gestures, help to avoid unreasonable gestures, and improve the user’s interactive experience.
American Psychological Association (APA)
Ma, Fujun& Song, Fanghao& Liu, Yan& Niu, Jiahui. 2020. sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1138904
Modern Language Association (MLA)
Ma, Fujun…[et al.]. sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1138904
American Medical Association (AMA)
Ma, Fujun& Song, Fanghao& Liu, Yan& Niu, Jiahui. sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1138904
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138904