A Deep Learning Approach for Mental Fatigue State Assessment

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 2; p. 555
• Main Authors: Fan, Jiaxing, Dong, Lin, Sun, Gang, Zhou, Zhize
• Format: Journal Article
• Language: English
• 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
• DOI: 10.3390/s25020555
• ISSN: 1424-8220

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.