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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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 25; no. 2; p. 555
Main Authors Fan, Jiaxing, Dong, Lin, Sun, Gang, Zhou, Zhize
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.01.2025
MDPI
Subjects
Accuracy
Algorithms
Artificial intelligence
Cardiovascular disease
Classification
Deep Learning
deep neural network
ECG
Efficiency
Electrocardiogram
Electrocardiography
Exercise intensity
Fatigue
Fourier transforms
Heart
Heart beat
Heart Rate - physiology
Humans
Injuries
Kinematics
mental fatigue
Mental Fatigue - diagnosis
Mental Fatigue - physiopathology
Nervous system
Neural networks
Neural Networks, Computer
Physiological aspects
Physiology
Questionnaires
Signal Processing, Computer-Assisted
Support Vector Machine
Time series
Wavelet transforms
ECG
electrocardiogram
mental fatigue
deep neural network
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Summary: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.
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These authors contributed equally to this work.
ISSN:1424-8220
1424-8220
DOI:10.3390/s25020555