Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

Study on the effect of human sympathetic nerve cold stimulation to relieve driving fatigue based on order recurrence plot

Driving fatigue is very likely to cause traffic accidents, seriously threatening the lives and properties of drivers. Therefore, accurate detection and effective mitigation of driving fatigue are crucial for ensuring the personal safety of drivers. This study proposes a method to relieve driving fatigue by properly reducing the temperature to stimulate the human sympathetic nerve. The method uses the intelligent cooling and blowing device on the car seat cushion to achieve cold stimulation of the sympathetic nerve of the driver by reducing the temperature of the driver's hip, back and neck, so as to increase the excitement of the sympathetic nerve, keep the driver alert and achieve the purpose of fighting driving fatigue. In view of the fact that the traditional fatigue detection method is easily affected by environmental factors and individual differences, this study uses the order recurrence plot (ORP) method to detect driving fatigue based on electroencephalogram (EEG) signals. The results show that ORP textures drawn by EEG signals of the two driving conditions (normal driving condition and sensory cold stimulation driving condition) are significantly different, and the quantization parameters determinism (DET) and average diagonal line length (DLL) values are significantly different. Cold stimulation of the subjects' hips, back and neck to alleviate driving fatigue was the best when the temperature was 21 °C. In addition, compared with the traditional methods of fatigue relief, the sensory cold stimulation method proposed in this study does not easily to produce tolerance and has no damage to the body.

A systematic review of physiological signals based driver drowsiness detection systems

Driving a vehicle is a complex, multidimensional, and potentially risky activity demanding full mobilization and utilization of physiological and cognitive abilities. Drowsiness, often caused by stress, fatigue, and illness declines cognitive capabilities that affect drivers' capability and cause many accidents. Drowsiness-related road accidents are associated with trauma, physical injuries, and fatalities, and often accompany economic loss. Drowsy-related crashes are most common in young people and night shift workers. Real-time and accurate driver drowsiness detection is necessary to bring down the drowsy driving accident rate. Many researchers endeavored for systems to detect drowsiness using different features related to vehicles, and drivers' behavior, as well as, physiological measures. Keeping in view the rising trend in the use of physiological measures, this study presents a comprehensive and systematic review of the recent techniques to detect driver drowsiness using physiological signals. Different sensors augmented with machine learning are utilized which subsequently yield better results. These techniques are analyzed with respect to several aspects such as data collection sensor, environment consideration like controlled or dynamic, experimental set up like real traffic or driving simulators, etc. Similarly, by investigating the type of sensors involved in experiments, this study discusses the advantages and disadvantages of existing studies and points out the research gaps. Perceptions and conceptions are made to provide future research directions for drowsiness detection techniques based on physiological signals.

Study on the Effect of Judgment Excitation Mode to Relieve Driving Fatigue Based on MF-DFA

Driving fatigue refers to a phenomenon in which a driver’s physiological and psychological functions become unbalanced after a long period of continuous driving, and their driving skills decline objectively. The hidden dangers of driving fatigue to traffic safety should not be underestimated. In this work, we propose a judgment excitation mode (JEM), which adds secondary cognitive tasks to driving behavior through dual-channel human–computer interaction, so as to delay the occurrence of driving fatigue. We used multifractal detrended fluctuation analysis (MF-DFA) to study the dynamic properties of subjects’ EEG, and analyzed the effect of JEM on fatigue retardation by Hurst exponent value and multifractal spectrum width value. The results show that the multifractal properties of the two driving modes (normal driving mode and JEM) are significantly different. The JEM we propose can effectively delay the occurrence of driving fatigue, and has good prospects for future practical applications.

Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals

Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series using long-term MSE as reference. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions-as a function of time series length-present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.