A Deep Learning Approach for Mental Fatigue State Assessment
This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/555 |
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| author | Jiaxing Fan Lin Dong Gang Sun Zhize Zhou |
| author_facet | Jiaxing Fan Lin Dong Gang Sun Zhize Zhou |
| author_sort | Jiaxing Fan |
| collection | DOAJ |
| description | This study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.29% in identifying fatigue from original ECG data, 2D spectral characteristics and physiological information of subjects. In comparison to traditional methods, such as Support Vector Machines (SVMs) and Random Forests (RFs), as well as other deep learning methods, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), the proposed approach demonstrates significantly improved experimental outcomes. Overall, this study offers a promising solution for accurately recognizing fatigue through the analysis of physiological signals, with potential applications in sports and physical fitness training contexts. |
| format | Article |
| id | doaj-art-e2b931c1dfda42f78916e3801818011c |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e2b931c1dfda42f78916e3801818011c2025-01-24T13:49:20ZengMDPI AGSensors1424-82202025-01-0125255510.3390/s25020555A Deep Learning Approach for Mental Fatigue State AssessmentJiaxing Fan0Lin Dong1Gang Sun2Zhize Zhou3Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, ChinaInstitute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, ChinaInstitute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, ChinaInstitute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, ChinaThis study investigates mental fatigue in sports activities by leveraging deep learning techniques, deviating from the conventional use of heart rate variability (HRV) feature analysis found in previous research. The study utilizes a hybrid deep neural network model, which integrates Residual Networks (ResNet) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, and a transformer for feature fusion. The model achieves an impressive accuracy of 95.29% in identifying fatigue from original ECG data, 2D spectral characteristics and physiological information of subjects. In comparison to traditional methods, such as Support Vector Machines (SVMs) and Random Forests (RFs), as well as other deep learning methods, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), the proposed approach demonstrates significantly improved experimental outcomes. Overall, this study offers a promising solution for accurately recognizing fatigue through the analysis of physiological signals, with potential applications in sports and physical fitness training contexts.https://www.mdpi.com/1424-8220/25/2/555mental fatiguedeep neural networkelectrocardiogramECG |
| spellingShingle | Jiaxing Fan Lin Dong Gang Sun Zhize Zhou A Deep Learning Approach for Mental Fatigue State Assessment Sensors mental fatigue deep neural network electrocardiogram ECG |
| title | A Deep Learning Approach for Mental Fatigue State Assessment |
| title_full | A Deep Learning Approach for Mental Fatigue State Assessment |
| title_fullStr | A Deep Learning Approach for Mental Fatigue State Assessment |
| title_full_unstemmed | A Deep Learning Approach for Mental Fatigue State Assessment |
| title_short | A Deep Learning Approach for Mental Fatigue State Assessment |
| title_sort | deep learning approach for mental fatigue state assessment |
| topic | mental fatigue deep neural network electrocardiogram ECG |
| url | https://www.mdpi.com/1424-8220/25/2/555 |
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