Yawn Detection for Driver’s Drowsiness Prediction Using Bi-Directional LSTM with CNN Features

Saurav, Sumeet; Mathur, S.S.; Sang, Ishan; Prasad, Shyam Sunder; Singh, Sanjay

doi:10.1007/978-3-030-44689-5_17

Cited by 11 publications

(18 citation statements)

References 15 publications

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“…Nature of dataset: The existing works [ 14 , 15 , 16 , 17 , 18 ] utilized data from a simulated environment, in which the data may not reflect the real-world environment. Our work considered a real-world dataset, which verifies the validity of the prediction model in real-world deployment.…”

Section: Results and Comparisonmentioning

confidence: 99%

“…Dataset: 11–45 participants contributed the datasets in existing works [ 14 , 15 , 16 , 17 , 18 ]. Although the dataset in our work included 108 participants, all of them are small-scale datasets.…”

Section: Results and Comparisonmentioning

confidence: 99%

“…Features: The feature extraction approaches can be categorized into image-based [ 14 , 15 , 18 ] and biometric-signal-based approaches [ 16 , 17 ] (and ours). Statistics or signal processing techniques were involved to compute the features in [ 14 , 17 , 18 ] (and ours), whereas deep learning was adopted in [ 15 , 16 ]. One study reported on the issue of data quality for images and EEG [ 51 ].…”

Section: Results and Comparisonmentioning

confidence: 99%

“…The authors’ results reported the recall and precision to be 96.1% and 98.6%, respectively. Another work extracted the features of images using convolutional neural networks (CNNs) and built a prediction model using a long short-term memory (LSTM) network [ 15 ]. An accuracy of 75% was achieved for a prediction of 3–5 s in advance.…”

Section: Introductionmentioning

confidence: 99%

“…Single-split validation: Some works did not adopt cross-validation as model validation in which one split validation [ 14 ] and not specified [ 15 , 18 , 19 ] were found. Limited data were evaluated or biased results may have obtained with certain groups of training and testing datasets.…”

Section: Introductionmentioning

confidence: 99%

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Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Chui¹,

Gupta

Liu

et al. 2021

Sensors

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Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

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