Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver’s drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.

Drowsiness during driving is a severe problem that must be addressed to improve road safety. Numerous counter-measures have been proposed to resolve this issue like adaptive environmental settings (temperature, sound, and light). The objective of this study was to accurately predict the effects of exposure to different colors of light on human drowsiness by using functional near-infrared spectroscopy and other physical measurements (heart rate and eye closure). We targeted two regions of the brain (visual and prefrontal cortices). Twenty-three healthy subjects were investigated to evaluate all variables related to the awakening state, and twenty-one healthy subjects were also examined in the drowsy state evaluation. Eventually, the ten most suitable subjects were exposed to red, green, and blue lights under drowsy conditions, according to the experimental paradigm. Dim light was maintained in the experimental premises before and after colored light exposure to limit the results to those produced only in response to the desired stimuli. Eye closure, heart rate, and changes in oxy and deoxy hemoglobin concentrations were measured to characterize the condition (awake/drowsy) of the subject. A support vector machine classifier was used to identify the classification accuracy of awake and drowsy states. In conclusion, exposure to blue light triggered the activation of oxy hemoglobin in targeted brain regions; however, deoxy hemoglobin was not significantly affected by exposure to any of the colored lights. Noticeably, our study revealed that blue light exposure is more effective at reducing drowsiness than exposure to red and green lights.INDEX TERMS functional near-infrared spectroscopy, colored light exposure, drowsiness, sleep deprivation, heart rate, and eye closure.

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Photoacoustic imaging (PAI) is an emerging nondestructive testing technique to evaluate ever-growing steel products and structures for safety and reliability. In this study, we have analyzed steel material with inbuilt cracks using computer-aided numerical simulations, imitating the PAI methodology. Cracks are introduced in a steel cylinder along three axes at different locations, and then a finite element method simulation in Abaqus software is performed to generate an acoustic wave and read it back at sensing locations after passing through the crack. The data are observed, analyzed, and modeled using the composite sine wave data fitting modeling technique. Afterwards, the Nelder–Mead simplex method is used to optimize the parameters of the model. It is concluded that with the change in the crack location, there is a change in the model parameters such as amplitude and frequencies. Results for cracks at seven different locations along each of the three axes are added, and listed in tabular form to present an analysis and comparison of the changes in the modeled parameters with respect to these crack locations.