Driver fatigue analysis based on upper body posture and DBN-BPNN model

Zheng, Ziyuan; Dai, Shenghua; Yao, Liang; Xie, Xuxu

doi:10.1109/iaeac47372.2019.8997925

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…The behaviour recognition module is based on a new dual-input 3DCNN model that is integrated into the Raspberry Pi. Zheng et al [ 77 ] proposed a novel DBN-BPNN model in which the deep belief network (DBN) was used to extract feature set, and BPNN was the classifier. The average accuracy of this model can achieve 92.75%.…”

Section: Detection Methods Of Train Driver Fatigue and Distractionmentioning

confidence: 99%

Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

Fan

Huang

Lin

et al. 2022

Computational Intelligence and Neuroscience

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Train drivers’ inattention, including fatigue and distraction, impairs their ability to drive and is the major risk factor for human-caused train accidents. Many experts have undertaken numerous studies on train driver exhaustion and distraction, but a systematic study is still missing. Through a systematic review, this work aims to outline the types, risk factors, consequences, and detection methods of train driver fatigue and distraction. The effects of central nervous fatigue and cognitive distraction in train drivers during driving are caused by rest and sleep schedules, workload, automation levels, and mobile phones. Furthermore, train drivers’ fatigue and distraction can cause loss of concentration and slow reaction, resulting in dangerous driving behaviour such as speeding and SPAD. Researchers have combined subjective reporting, physiological parameters, and physical factors to construct detection algorithms with good results to detect train driver fatigue and distraction. This review offers recommendations for researchers looking into train driver fatigue and distraction. And it can also make valuable recommendations for future studies about railway traffic safety.

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Section: Detection Methods Of Train Driver Fatigue and Distractionmentioning

confidence: 99%

Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

Fan

Huang

Lin

et al. 2022

Computational Intelligence and Neuroscience

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“…3 depicts the three-dimensional graph of upper body posture detection under alert and fatigue states. Moreover, in the research by Ziyuan Zheng [17] et al, key point connections related to human fatigue activities were summarized. Therefore, this study selects key points from 0 to 16 to assist in fatigue detection.…”

Section: Body Feature Extractionmentioning

confidence: 99%

DA-ViViT: Fatigue detection framework using joint and facial keypoint features with dynamic distributed attention video vision transformer

Deng,

Yang,

Guo

et al. 2024

Preprint

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Fatigue driving stands as a primary contributor to severe traffic accidents. Despite notable advancements in fatigue monitoring techniques, current methods frequently overlook the utilization of shifts in drivers' body postures induced by fatigue. These postural changes can offer valuable indicators of drivers' levels of alertness and degree of fatigue. Therefore, a real-time system with the ability to process body information is needed. This study presents a novel framework that integrates facial fatigue characteristics with alterations in drivers' upper body postures to comprehensively monitor fatigue across multiple dimensions. The neural network architecture at the core of the proposed approach utilizes a video vision transformer framework, with the self-attention mechanism optimized to accommodate the magnitude of fatigue-induced posture changes. This optimization ensures high accuracy while simultaneously reducing computational expenses. This gives the approach the potential for embedded deployment. According to requirements, the standard DMD dataset captured in real-world driving scenarios has been selected for validating the proposed method. In terms of accuracy, compared to detection methods that solely rely on facial and upper body poses, the multi-feature fatigue detection approach has achieved an improvement of 3.4% and 11.9%, reaching 96.24%. Additionally, it has been observed that the adoption of the Dynamic Distributed Attention-based backbone has led to a significant increase in inference speed of over 30%.

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Behavior-based driver fatigue detection system with deep belief network

Savaş

Becerikli

2022

Neural Comput & Applic

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Driver fatigue analysis based on upper body posture and DBN-BPNN model

Cited by 6 publications

References 7 publications

Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

DA-ViViT: Fatigue detection framework using joint and facial keypoint features with dynamic distributed attention video vision transformer

Behavior-based driver fatigue detection system with deep belief network

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