Drowsiness Detection by Thoracic Effort Signal Analysis with Professional Drivers in Real Environments

Objective: The aim of this work is to develop a new index to assess the alertness state of drivers based on the respiratory dynamics derived from an inductive band. Background: Detection of drowsiness while driving is a leading objective in advanced driver assistance systems. A biomedical variable like thoracic effort, which is related to autonomic nervous system, provides direct information of the driver physiological state, instead of indirect indicia of the participant's behavior. Therefore, they may be especially useful to collect detailed information of the drowsiness state and anticipate risky situations while driving. Method: The experiment described in this paper allows us to study how drivers react in strenuous conditions, in which they tend to get fatigued and drowsy, but struggle against falling asleep. The respiration data used in this study was recordered by doing 72 hours of simulation driving test and 100 hours of real vehicle tests in real environment. Results: The results demonstrate the viability of drowsiness detection in real vehicle using thoracic effort signal. The proposed method has a sensitivity of 93.7% and specificity of 86.3% in detecting full awake drivers while it has a sensitivity of 83.1% and specificity of 95.3% in detecting drowsy drivers. Conclusions: The proposed index may be promising to assess the alertness state of real drivers. Applications: The potential applications of the algorithm are to detect drowsiness states while driving and give an alarm in commercial fleets or professional drivers to assure the integrity of the driver. Key Words— Alertness state, Advanced Driver Assistance Systems, Fatigue, Fleet, and Inattention.