Head movement-based driver drowsiness detection: A review of state-of-art techniques

Mittal, Ajay; Kumar, K. Mohan; Dhamija, Sarina; Kaur, Manvjeet

doi:10.1109/icetech.2016.7569378

Cited by 67 publications

(23 citation statements)

References 23 publications

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“…Later on, a sensor-based device called the steering wheel monitoring system (SAM) was developed to detect driving fatigue by monitoring the abnormal movement of the vehicle's steering wheel [10]. Although detection of steering wheel features good real-time performance and low cost, it also suffers poor anti-interference ability and low reliability [11].…”

Section: Introductionmentioning

confidence: 99%

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Chen

et al. 2019

Computational Intelligence and Neuroscience

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The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.

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Section: Introductionmentioning

confidence: 99%

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Chen

et al. 2019

Computational Intelligence and Neuroscience

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“…The facial expressions of drivers are obvious clues for fatigue driving detection, based on the eye state analysis, Wierwille and Ellsworth [20] proposed a physical parameter – PERCLOS (Percentage of Eyelid Closure over the Pupil), which is widely used to evaluate the driving state of drivers. In addition, the head movement of the driver can also reflect his fatigue, Mittal et al [21] placed an inclinometer on driver's forehead to obtain head pose, and found that the fast downward movement of their head is an important feature of fatigue driving. It is also found that driving behaviours of driver show the great difference between fatigue state and normal state, Hailin et al [22] presented that when a driver drowsy, his driving behaviours such as accelerate, brake, shift and steer will change abnormally.…”

Section: Related Workmentioning

confidence: 99%

Fatigue driving detection model based on multi‐feature fusion and semi‐supervised active learning

Lin

Wang

et al. 2019

IET Intelligent Transport Systems

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“…Deteksi perilaku pengemudi merupakan cara yang paling banyak digunakan karena tidak membutuhkan perancangan sistem yang kompleks. Salah satu faktor yang dapat dideteksi dari pengemudi yang mengantuk adalah perubahan posisi kepala [4]. Posisi kepala akan tegak jika pengemudi dalam kondisi tubuh normal dan akan mengalami perubahan posisi secara tiba -tiba jika pengemudi mengantuk.…”

Section: Pendahuluanunclassified

Sistem Peringatana Dini Menggunakan Deteksi Kemiringan Kepala pada Pengemudi Kendaraan Bermotor yang Mengantuk

Amirullah¹,

Kusuma²,

Tasripan³

2019

JTITS

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Tingginya tingkat kecelakaan yang disebabkan oleh human error masih menjadi permasalahan yang dihadapi di Indonesia. Sebesar 69,7% kecelakaan kendaraan bermotor disebabkan oleh human error. Kondisi pengemudi yang mengantuk merupakan salah satu faktor penyebab human error, sehingga diperlukan sebuah sistem keamanan dalam berkendara untuk menekan permasalahan tersebut. Ketika seseorang berada dalam kondisi mengantuk, terdapat kondisi khusus yang dapat diidentifikasi. Salah satu kondisi tersebut adalah perubahan posisi kepala yang terjadi secara tiba-tiba. Pada tugas akhir ini, dirancang suatu sistem peringatan dini untuk pengemudi yang mengantuk berdasarkan perubahan posisi kepala (perubahan sebesar 10 cm dalam waktu 18 ms). Sistem dirancang wearable dalam bentuk headgear yang dilengkapi dengan sensor accelerometer MPU-6050 GY-521 dan dipasang pada kepala pengemudi mobil. Proses pengenalan kondisi kantuk dapat diperoleh dengan cara mengidentifikasi perubahan nilai percepatan pada sumbu X, sumbu Y, dan sumbu Z yang terjadi serentak. Perubahan posisi kepala untuk kondisi kantuk dideteksi dengan cara memberikan nilai ambang (threshold) untuk tiap sumbu. Deteksi kantuk yang dilakukan di dalam mobil, dilakukan sebanyak 10 kali pergerakan kepala, dengan nilai ambang untuk sumbu X sebesar 1, sumbu Ysebesar 3.5 dan sumbu z sebesar 0.5. Dari 10 kali pergerakan kepala, 7 pergerakan kepala berhasil terdeteksi dan 3 pergerakan tidak berhasil terdeteksi. Persentase keberhasilan dari pengujian tersebut sebesar 70%.

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Head movement-based driver drowsiness detection: A review of state-of-art techniques

Cited by 67 publications

References 23 publications

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Fatigue driving detection model based on multi‐feature fusion and semi‐supervised active learning

Sistem Peringatana Dini Menggunakan Deteksi Kemiringan Kepala pada Pengemudi Kendaraan Bermotor yang Mengantuk

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