Abstract
Drowsiness while driving can lead to accidents that are related to the loss of perception during emergencies that harm the health. Among physiological signals, brain waves have been used as informative signals for the analyses of behavioral observations, steering information, and other biosignals during drowsiness. We inspected the machine learning methods for drowsiness detection based on brain signals with varying quantities of information. The results demonstrated that machine learning could be utilized to compensate for a lack of information and to account for individual differences. Cerebral area selection approaches to decide optimal measurement locations could be utilized to minimize the discomfort of participants. Although other statistics could provide additional information in further study, the optimized machine learning method could prevent the dangers of drowsiness while driving by considering a transitional state with nonlinear features. Because brain signals can be altered not only by mental fatigue but also by health status, the optimization analysis of the system hardware and software will be able to increase the power-efficiency and accessibility in acquiring brain waves for health enhancements in daily life.
Highlights
The appropriate sample size for machine learning classifications could be determined by fitting a classifier learning curve produced using empirical data as described in a study about inverse power-law models [35]
If we focused on the results of the spectral features, Fp1 and Fp2 exhibited the highest values for 30-s EEG and 180-s EEG using random decision forest (RF) results and for 30-s EEG using support vector machine (SVM)
The present study used a 16-channel EEG with an RF to demonstrate the detection of drowsiness with varied spectral and nonlinear information quantities
Summary
The brain in the drowsy state has yielded to the slowness or cessation of body movement, as indicated by the etymology of drowsiness, such as “drūsian” and “dreosan” in Old English, which mean “sink” and “to become slow”. The state of the brain yields negative outcomes without an adequate self-awareness of global indicators such that driving in a drowsy state can lead to unintended damage, injury, and death, especially in view of the fact that automotive improvements in recent times have not considered safe in an adequate manner [1]. Many people continue to drive despite their self-awareness of sleep deprivation and its risks [2]. People underestimate the severity and risk caused by drowsiness [3]. Sleep-deprived people are not able to assess situations to prevent the occurrence of accidents
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