Study of Artificial Intelligence algorithms for the detection of drowsiness and yawning

Drowsiness and yawning can have negative impacts on road safety and lead to accidents. When a person is drowsy, their vigilance and reaction time can be significantly reduced, increasing the risk of driving errors, lack of attention, and even falling asleep at the wheel. Yawning is often associated...

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Bibliographic Details
Main Authors: Boumaiza, Zied, Mabrouk, Walid Ben, Kalboussi, Rahma, Njima, Chakib Ben
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence (AI)
Deep learning
drowsiness
Ear
Fatigue
Heart rate
Learning (artificial intelligence)
Lips
Training
Wheels
yawning
Online Access:Request full text
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Summary:Drowsiness and yawning can have negative impacts on road safety and lead to accidents. When a person is drowsy, their vigilance and reaction time can be significantly reduced, increasing the risk of driving errors, lack of attention, and even falling asleep at the wheel. Yawning is often associated with fatigue and can be a sign of imminent drowsiness. The integration of artificial intelligence (AI) can help mitigate these risks by providing innovative solutions. For example, drowsiness detection systems using AI technologies can analyze biometric signals, such as eye movements or heart rate, to detect signs of fatigue in the driver. When drowsiness is detected, the system can emit audible or vibratory warnings to alert the driver and recommend taking a break.
ISSN:2767-9896
DOI:10.1109/ICCAD60883.2024.10554012