Real-Time Driver Fatigue Detection from ECG Using Deep Learning Algorithm

Cherian, V. Anjaly; Bhardwaj, Rahul; Balasubramanian, Venkatesh

doi:10.1007/978-981-15-9054-2_71

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Electrocardiography (ECG) and photoelectric volumetric pulse tracing (PPG) can be regarded as suitable methods for detecting fatigue accurately. Cherian et al [36] proposed a real-time fatigue-detection approach based on heart rate variability (HRV) obtained from ECG preprocessing. They developed a deep-learning network model comprising an autoencoder network based on unsupervised learning to classify HRV features (frequency and time domains).…”

Section: ) Fatigue Detection Based On Heart Signalsmentioning

confidence: 99%

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Fatigue Detection for Ship OOWs Based on Input Data Features, From the Perspective of Comparison With Vehicle Drivers: A Review

et al. 2023

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Ninety percent of the world's cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicles drivers owing to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analysed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organised based on input sources, which include the physiological/behavioural features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarised. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analysed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a non-negligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behaviour videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realise quantitative, accurate, and real-time fatigue detections in OOWs on actual ships.

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Section: ) Fatigue Detection Based On Heart Signalsmentioning

confidence: 99%

“…The purpose of fatigue detection should be to remind the driver in time to realize early warning and avoid the danger caused by fatigue [36,42]. For ship OOWs, fatigue detection and timely warning are the current and future research focus.…”

Section: Detection and Early Warningmentioning

confidence: 99%

Fatigue Detection for Ship OOWs Based on Input Data Features, From the Perspective of Comparison With Vehicle Drivers: A Review

et al. 2023

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“…Physiological methods can be identified by physical measures obtained from the human body, such as brain activity, detected by an electroencephalogram (EEG) [3][4][5][6][7][8]; heartbeat, measured by an electrocardiogram (ECG) [9][10][11]; eye signals, identified by an electrooculogram (EOG) [12,13]; and electrical muscle signal detection, referred to as electromyography (EMG) [14][15][16]. Due to their information richness, these physiological approaches have a high level of accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features

2022

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Modern cities have imposed a fast-paced lifestyle where more drivers on the road suffer from fatigue and sleep deprivation. Consequently, road accidents have increased, becoming one of the leading causes of injuries and death among young adults and children. These accidents can be prevented if fatigue symptoms are diagnosed and detected sufficiently early. For this reason, we propose and compare two AlexNet CNN-based models to detect drivers’ fatigue behaviors, relying on head position and mouth movements as behavioral measures. We used two different approaches. The first approach is transfer learning, specifically, fine-tuning AlexNet, which allowed us to take advantage of what the model had already learned without developing it from scratch. The newly trained model was able to predict drivers’ drowsiness behaviors. The second approach is the use of AlexNet to extract features by training the top layers of the network. These features were reduced using non-negative matrix factorization (NMF) and classified with a support vector machine (SVM) classifier. The experiments showed that our proposed transfer learning model achieved an accuracy of 95.7%, while the feature extraction SVM-based model performed better, with an accuracy of 99.65%. Both models were trained on a simulated NTHU Driver Drowsiness Detection dataset.

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A survey of fatigue measures and models

Laverghetta

Tran

Braynen

et al. 2023

Journal of Defense Modeling & Simulation

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In long, stressful operational periods, military personnel face numerous challenges that may compromise their performance, an especially important one being fatigue. Current literature supports the view that behavioral, physiological, and cognitive factors are all predictive of the level of fatigue in individuals. However, much of the work on modeling fatigue has taken a narrow approach, relying only on a handful of modalities to measure fatigue. This paper aims to fill the void by providing an extensive overview of the current literature on both computationally measuring and modeling fatigue. We provide up-to-date and practical advice on which models are best suited for different situations and highlight directions for future work.

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Real-Time Driver Fatigue Detection from ECG Using Deep Learning Algorithm

Cited by 3 publications

References 12 publications

Fatigue Detection for Ship OOWs Based on Input Data Features, From the Perspective of Comparison With Vehicle Drivers: A Review

Fatigue Detection for Ship OOWs Based on Input Data Features, From the Perspective of Comparison With Vehicle Drivers: A Review

A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features

A survey of fatigue measures and models

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