AIMC Topic: Fatigue
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A machine learning model using clinical notes to identify physician fatigue.
Nature communications
Jul 1, 2025
Clinical notes should capture important information from a physician-patient encounter, but they may also contain signals indicative of physician fatigue. Using data from 129,228 emergency department (ED) visits, we train a model to identify notes wr...
Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning.
BMC medical informatics and decision making
Jun 3, 2025
BACKGROUND: Fatigue is a prevalent and debilitating symptom of non-communicable diseases (NCDs); however, its biological basis are not well-defined. This exploratory study aimed to identify key biological drivers of fatigue by integrating metabolomic...
Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion.
Sensors (Basel, Switzerland)
May 20, 2025
The physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination involves measuring physiological responses such as oxygen uptake (VO) while performing the work. The ...
Dynamic cross-domain transfer learning for driver fatigue monitoring: multi-modal sensor fusion with adaptive real-time personalizations.
Scientific reports
May 6, 2025
Driver fatigue is one of the most common causes of road accidents, which means that there is a great need for robust and adaptive monitoring systems. Current models of fatigue detection suffer from domain-specific limitations in generalizing across d...
A dense multi-pooling convolutional network for driving fatigue detection.
Scientific reports
May 3, 2025
Driver fatigue is one of the major causes of traffic accidents, particularly for drivers of large vehicles, who are more susceptible to fatigue due to prolonged driving hours and monotonous conditions during their journeys. Existing vision-based driv...
Optimized driver fatigue detection method using multimodal neural networks.
Scientific reports
Apr 10, 2025
Driver fatigue is a significant factor contributing to road accidents, highlighting the need for precise and reliable detection systems. This study introduces a comprehensive approach using multimodal neural networks, leveraging the DROZY dataset, wh...
DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning.
Journal of neural engineering
Apr 7, 2025
. Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue detection in real-world scenarios are often challenging, particularly in cases involving subjects not...
Influence pathways of noise exposure on people's negative emotions and health across different activity contexts: A neural network-based double machine learning approach.
Health & place
Mar 30, 2025
Noise is a major global environmental issue that raises concerns about both mental and physical health. However, few studies have investigated the mediating role of emotions in the pathways linking noise exposure to health outcomes. Additionally, man...
Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional bioelectronics.
Biosensors & bioelectronics
Mar 17, 2025
Quantitatively measuring human mental states that profoundly affect cognition, behavior, and recovery would revolutionize personalized digital healthcare. Detecting fatigue, stress, and sleep is particularly important due to their interdependence: pe...
Subjective recovery in professional soccer players: A machine learning and mediation approach.
Journal of sports sciences
Feb 5, 2025
Coaches often ask players to judge their recovery status (subjective recovery). We aimed to explore potential determinants of subjective recovery in 101 male professional soccer players of 4 Italian Serie C teams and to further investigate whether th...
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