Recently, the ability to monitor driver drowsiness has attracted a great deal of attention in the automotive industry, in order to prevent the risk due to an inadequate driver psycho-physical state. Specifically, the research effort has focused on the study of the physiological signals to assess the attention level. The main idea consists in verifying the drowsiness level through analyzing the Heart Rate Variability ( HRV). The HRV allows to understand the activity of the autonomic nervous system that regulates a series of unconscious and involuntary activities ( e. g. the heartbeat, the blood pressure). The HRV is traditionally obtained from electrocardiography ( ECG) even though the photoplethysmography ( PPG) signal has been proposed as valid alternative to ECG in order to overcome some limitations derived from it. For the above reasons, we analyzed the skin micro-movements and changes in facial color due to blood circulation quite indistinguishable with naked eye in order to extract facial landmarks and to reconstruct PPG signal. The results we obtained by validation confirmed the correlation between the PPG signal detected by sensors and the reconstructed PPG signal from facial landmarks.
|Titolo:||Advanced Motion-Tracking System with Multi-Layers Deep Learning Framework for Innovative Car-Driver Drowsiness Monitoring|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|