To assure aviation safety: the pilot fatigue detection based on short-term multimodal physiological signals [PDF]
Frontiers in Human NeurosciencePilot fatigue detection based on physiological signals is practical for aviation safety. Current methods face challenges in balancing the high computational cost of deep learning models with robust accuracy, especially when integrating short-term ...
Kai Chen +5 more
doaj +2 more sources
A regression method for EEG-based cross-dataset fatigue detection [PDF]
Frontiers in Physiology, 2023 Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of electroencephalogram (EEG) data for training, which is resource-consuming ...
Duanyang Yuan +5 more
doaj +2 more sources
Fatigue Detection with Spatial-Temporal Fusion Method on Covariance Manifolds of Electroencephalography [PDF]
Entropy, 2021 With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths.
Nan Zhao +6 more
doaj +2 more sources
Fatigue Detection Using Computer Vision [PDF]
International Journal of Electronics and Telecommunications, 2010 Fatigue Detection Using Computer VisionLong duration driving is a significant cause of fatigue related accidents of cars, airplanes, trains and other means of transport. This paper presents a design of a detection system which can be used to detect fatigue in drivers. The system is based on computer vision with main focus on eye blink rate.
Mitesh Patel +3 more
openaire +3 more sources
Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network [PDF]
Sensors, 2023 Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes.
Lili Xu, Jizu Li, Ding Feng
doaj +2 more sources
Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection [PDF]
SensorsIn this paper, we propose a novel few-shot optimization with Hybrid Euclidean Distance with Large Language Models (HED-LM) to improve example selection for sensor-based classification tasks.
Elsen Ronando, Sozo Inoue
doaj +2 more sources
Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection [PDF]
SensorsDrowsy 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 ...
Zhen Gao +5 more
doaj +2 more sources
Remote Photoplethysmography and Motion Tracking Convolutional Neural Network with Bidirectional Long Short-Term Memory: Non-Invasive Fatigue Detection Method Based on Multi-Modal Fusion [PDF]
SensorsExisting vision-based fatigue detection methods commonly utilize RGB cameras to extract facial and physiological features for monitoring driver fatigue. These features often include single indicators such as eyelid movement, yawning frequency, and heart ...
Lingjian Kong +4 more
doaj +2 more sources
A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems
Sensors, 2021 Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system.
Igor Stancin, Mario Cifrek, Alan Jovic
doaj +1 more source
Efficient and Robust Driver Fatigue Detection Framework Based on the Visual Analysis of Eye States
Promet (Zagreb), 2023 Fatigue detection based on vision is widely employed in vehicles due to its real-time and reliable detection results. With the coronavirus disease (COVID-19) outbreak, many proposed detection systems based on facial characteristics would be unreliable ...
Yancheng Ling, Xiaoxiong Weng
doaj +1 more source