Validity of facial features’ geometric measurements for real-time assessment of mental fatigue in construction equipment operators

Mehmood, Imran; Li, Heng; Umer, Waleed; Arsalan, Aamir; Shakeel, M. Saad; Anwer, Shahnawaz

doi:10.1016/j.aei.2022.101777

Cited by 22 publications

(7 citation statements)

References 84 publications

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“…The core of this extraction method is to calculate the driver's eye aspect ratio (EAR) and mouth aspect ratio (MAR) based on the driver's facial key point. The calculation method of EAR is shown in Figure 4 and formula (1) [23]. The calculation method of MAR is shown in Figure 4 and formula (2) [23].…”

Section: Eye Aspect Ratio and Mouth Aspect Ratiomentioning

confidence: 99%

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Computer Vision-based Driver Fatigue Detection Framework with Personalization Threshold and Multi-feature Fusion

Lin

Yang

2023

Preprint

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This paper proposes a novel non-intrusive and computer vision-based framework for driver fatigue detection from the video. First, to improve the judging accuracyof the driver’s facial expressions, the personalized threshold is proposed insteadof the traditional average threshold. Secondly, in order to alleviate the impactof the lack of relevant public data on model training, the transfer learning isused to train the eye and mouth state classifier. Finally, to solve the problem oflow universality and accuracy of the driver fatigue detection caused caused byusing only one type of facial features, multiple features including appearance-based features and deep learning-based features are used to detect driver fatiguedriving. The experiment results indicate that our method achieves 92.21% F1score and 29 fps, and yields better performance than traditional methods on thepublic NTHU-DDD dataset.

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Section: Eye Aspect Ratio and Mouth Aspect Ratiomentioning

confidence: 99%

“…The calculation method of EAR is shown in Figure 4 and formula (1) [23]. The calculation method of MAR is shown in Figure 4 and formula (2) [23].…”

Section: Eye Aspect Ratio and Mouth Aspect Ratiomentioning

confidence: 99%

Computer Vision-based Driver Fatigue Detection Framework with Personalization Threshold and Multi-feature Fusion

Lin

Yang

2023

Preprint

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“…Their proposed framework's ability to classify cognitive fatigue states was shown to correspond with self-reported fatigue states at an accuracy rate of approximately 89%. Mehmood et al (2023) [35] worked on the classification of mental fatigue states of construction equipment operators using EEG data. They achieved the best performance accuracy of 99.94% from the bidirectional LSTM model.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Mental Workload Classification in Neuroscience: Deep Learning Approaches

Cakar,

Yavuz

2024

Preprint

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This study aims to classify mental workload levels from n-back cognitive data by employing a diverse set of deep learning approaches capable of accommodating both dense and sparse features. The n-back task paradigm provides rich temporal data, capturing information on working memory and cognitive demand from the same subjects over time across different n-back conditions. By integrating deep learning techniques that leverage both dense and sparse features, this research introduces novel perspectives tailored to the data structure of the n-back task. Our findings highlight the effectiveness of the extreme Deep Factorization Machine model with stratified 5-fold cross-validation. Compared to the baseline model for the 0- vs 1-back classification task, this approach achieved significant improvements: 67.50% in accuracy, 68.74% in sensitivity, 66.24% in specificity, and 68.48% in F1-Score among the models in which dense features are the combinations of hemodynamic measures and experimental variable, and subject is considered as a sparse feature. Additionally, employing Principal Component Analysis (PCA) resulted in significant enhancements in performance metrics compared to the baseline Logistic Regression model. Specifically, the utilization of PCA led to remarkable improvements of 53.03% in accuracy, 98.03% in sensitivity, 24.14% in specificity, and 70.37% in F1-Score, as observed in the classification of the 0- vs 1-back condition when using the xDeepFM model. These results underscore the utility of deep learning methods in accurately discerning mental workload levels from complex n-back cognitive science data, offering valuable insights into cognitive functioning and workload assessment.

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“…According to the literature, there is a substantial association between these signals and workers' mental states, and they can be employed to reliably identify construction workers' fatigue. Recently, geometric measurements of facial features have also been used to identify mental fatigue during on-site construction operations (Mehmood et al, 2022). However, among these technologies, EEG has emerged as one of the fastest-growing ones that has attracted significant attention from researchers for assessing workers' cognitive and mental states under dynamic workplace conditions (Zhang et al, 2019b).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based construction equipment operators’ mental fatigue classification using wearable EEG sensor data

Mehmood

Qarout

et al. 2023

Advanced Engineering Informatics

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Validity of facial features’ geometric measurements for real-time assessment of mental fatigue in construction equipment operators

Cited by 22 publications

References 84 publications

Computer Vision-based Driver Fatigue Detection Framework with Personalization Threshold and Multi-feature Fusion

Computer Vision-based Driver Fatigue Detection Framework with Personalization Threshold and Multi-feature Fusion

Enhancing Mental Workload Classification in Neuroscience: Deep Learning Approaches

Deep learning-based construction equipment operators’ mental fatigue classification using wearable EEG sensor data

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