Abstract
Drowsy driving is a prevalent and serious public health issue that deserves attention. Recent studies estimate that around 20% of car crashes have been caused by drowsy drivers. Nowadays, one of the main goals in the development of new advanced driver assistance systems is trustworthy drowsiness detection. In this paper, a drowsiness detection method based on changes in the respiratory signal is proposed. The respiratory signal, which has been obtained using an inductive plethysmography belt, has been processed in real time in order to classify the driver’s state of alertness as drowsy or awake. The proposed algorithm is based on the analysis of the respiratory rate variability (RRV) in order to detect the fight against to fall asleep. Moreover, a method to provide a quality level of the respiratory signal is also proposed. Both methods have been combined to reduce false alarms due to the changes of measured RRV associated not with drowsiness but body movements. A driving simulator cabin has been used to perform the validation tests and external observers have rated the drivers’ state of alertness in order to evaluate the algorithm performance. It has been achieved a specificity of 96.6%, a sensitivity of 90.3%, and Cohen’s Kappa agreement score of 0.75 on average across all subjects through a leave-one-subject-out cross-validation. A novel algorithm for driver’s state of alertness monitoring through the identification of the fight against to fall asleep has been validated. The proposed algorithm may be a valuable vehicle safety system to alert drowsiness while driving.
Highlights
Drowsiness is an intermediate state between wakefulness and sleep that may be defined as the progressive loss of cortical processing efficiency
Thoracic Effort Derived Drowsiness index (TEDD) ALGORITHM: DROWSINESS CLASSIFICATION RESULTS 1) TEDD ALGORITHM PARAMETERS TUNING The proposed algorithm has been optimized by tuning WLD and ThTedd parameter, which has been defined in Section III-C, in order to obtain the algorithm with the best drowsiness classification results
Whereas the best ThTedd has been searched by maximizing G or F1, the WLD optimization maximizes the area under the ROC curve (AUC)
Summary
Drowsiness is an intermediate state between wakefulness and sleep that may be defined as the progressive loss of cortical processing efficiency. Drowsy driving can be caused by a combination of sleep loss, driving when circadian rhythms are low (early morning hours or mid-afternoon) or for long periods of time. Drowsiness affects elements of human performance that are critical to safe driving such as: reaction time, alertness and information processing [2]. Drowsy driving is a prevalent and serious public health issue that deserves attention. The AAA Foundation for Traffic Safety in its 2015 Drowsy Driving Fact Sheet states that for the 2009-2015 period, the percentage of licensed drivers admitting drowsy driving (in the previous 30 days) has remained essentially constant, hovering around 30 percent. In the same report we can find that most (97%) American
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
