AIMC Topic: Fatigue
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EEG-based fatigue state evaluation by combining complex network and frequency-spatial features.
Journal of neuroscience methods
Feb 3, 2025
BACKGROUND: The proportion of traffic accidents caused by fatigue driving is increasing year by year, which has aroused wide concerns for researchers. In order to rapidly and accurately detect drivers' fatigue, this paper proposed an electroencephalo...
Application of Additive Manufacturing and Deep Learning in Exercise State Discrimination.
Sensors (Basel, Switzerland)
Jan 10, 2025
With the rapid development of sports technology, smart wearable devices play a crucial role in athletic training and health management. Sports fatigue is a key factor affecting athletic performance. Using smart wearable devices to detect the onset of...
Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection.
Sensors (Basel, Switzerland)
Dec 12, 2024
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and ...
Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye State.
Sensors (Basel, Switzerland)
Dec 6, 2024
Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives a complete analysis of a real-time, non-intrusive sleepiness detection system based on con...
Risk of crashes among self-employed truck drivers: Prevalence evaluation using fatigue data and machine learning prediction models.
Journal of safety research
Nov 16, 2024
INTRODUCTION: Transportation companies have increasingly shifted their workforce from permanent to outsourced roles, a trend that has consequences for self-employed truck drivers. This transition leads to extended working hours, resulting in fatigue ...
Multilevel attention mechanism for motion fatigue recognition based on sEMG and ACC signal fusion.
PloS one
Nov 4, 2024
This study aims to develop a cost-effective and reliable motion monitoring device capable of comprehensive fatigue analysis. It achieves this objective by integrating surface electromyography (sEMG) and accelerometer (ACC) signals through a feature f...
Development and validation of machine learning models for predicting cancer-related fatigue in lymphoma survivors.
International journal of medical informatics
Sep 14, 2024
BACKGROUND: New cases of lymphoma are rising, and the symptom burden, like cancer-related fatigue (CRF), severely impacts the quality of life of lymphoma survivors. However, clinical diagnosis and treatment of CRF are inadequate and require enhanceme...
Risk factors for depression in China based on machine learning algorithms: A cross-sectional survey of 264,557 non-manual workers.
Journal of affective disorders
Sep 5, 2024
BACKGROUND: Factors related to depression differ depending on the population studied, and studies focusing on the population of non-manual workers are lacking. Thus, we aimed to identify the risk factors related to depression in non-manual workers in...
Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm.
Scientific reports
Sep 4, 2024
Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots' precise fatigue detectio...
Research on low-power driving fatigue monitoring method based on spiking neural network.
Experimental brain research
Aug 23, 2024
Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detect...
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