Lighting environmental assessment in enclosed spaces based on emotional model

How natural light influences HSR drivers' visual behavior

Existing studies have shown that the lighting environment is essential in influencing a driver's visual behavior. Due to the pivotal role of high-speed railway (HSR) in worldwide transit, it is necessary to examine how HSR drivers' visual behavior adjust under different lighting environments. However, the methods for evaluating and categorizing lighting conditions have not been fully explored. In this study, we established a general framework for examining the impact of lighting on driver's visual behavior. The application of this framework to explore the effects of natural light on HSR drivers' visual characteristics was elaborated. Particularly, we used unsupervised machine learning methods to classify natural light conditions automatically. Specifically, Fuxing HSR simulation, illuminance meter, and Tobii Nano eye-tracker were employed to collect data. K-means clustering analysis of daily illuminance data identified 3 natural light conditions, namely low illuminance (1 pm-6 pm), medium illuminance (6 am-9 am), by and high illuminance (9 am-1 pm). Further, ANOVA with 3 natural light environments * 2 tunnel conditions * 4 areas of interest (AOIs) were conducted. Results manifested drivers' visual characteristics under different natural light conditions. Specifically, lower illuminance can lead to a wider average pupil diameter, while higher illuminance results in a greater number of fixations and saccades, and a shorter time to first fixation. Moreover, all the eye movement indicators are highest for the speed dial AOI. This study contributes to the field by developing a framework to examine the effects of lighting on drivers' visual behavior. The findings provide new insights into analyzing lighting environments by using machine learning methods, which servers to HSR driving safety and operational management.

Time-Dependent Effects of Altered Prebedtime Light Exposure in Enclosed Spaces on Sleep Performance Associated with Human States

Purpose

Exposure to artificial light influences human performance, which is essential for maintaining healthy work and sleep. However, existing research has not explored the intrinsic links between sleep performance and human states over time under prebedtime light exposure interventions (LEIs).

Methods

To investigate the time-dependent effects of altered prebedtime light exposure, four LEI groupings (#L1 - #L4) and a Time factor (D8, D9, and D10) were chosen for sleep experiments in enclosed spaces. Forty-eight young adults recruited were available for data analysis. Subjective alertness (SA), negative affect (NA), subjective sleep, and objective sleep were measured via the Karolinska Sleepiness Scale, Positive and Negative Affect Schedule, Next-day Self-assessment Sleep Quality, and joint assessment of wrist actigraphy and sleep diaries, respectively. Statistical analysis was used for the effects of light exposure on the human states (corresponding to the SA and NA) and sleep performance, while the process model helped construct the associations between the two.

Results

The statistical effects revealed that the Time had a significant main effect on subjective sleep and changes in prebedtime alertness; the LEI had a significant main effect only on sleep onset latency (SOL). After undergoing altered prebedtime light exposure, the mean SA increased at prebedtime of D9 (p = 0.022) and D10 (p = 0.044); No significant effect on the NA was observed; Mean subjective sleep had a significant increase from D8 to D10. Moreover, five actigraphy-estimated sleep parameters were interrelated. In light of this, a chained pathway relationship was identified. The SOL played a mediating predictor between prebedtime state and objective sleep, which was linked to the awakening state through subjective sleep.

Conclusion

Our study suggests that time-dependent effects of altered prebedtime light exposure on sleep performance are associated with human states at prebedtime and awakening, with implications for its prediction of sleep health.

Impact of lighting environment on human performance and prediction modeling of personal visual comfort in enclosed cabins

Enclosed cabins are of great significance in various fields, including national defense, scientific research, and industrial applications. It is important to clarify the impact of the lighting environment in these cabins on the people operating within them. This study investigated the effects of the lighting environment in enclosed cabins on the physiological, operational, and comfort performance of operators through simulated experiments. In Addition, using the Random Forest Algorithm and ExpandNet technique, we developed a prediction model to evaluate the comfort level of the lighting environment for personnel in enclosed cabins. The results indicated that pupil diameter exhibited the highest sensitivity to ambient light. The appropriate luminance combination of the screen and the ambient scene have a positive effect on human performance. In particular, it was observed that the average cognitive performance and comfort of participants tended to be relatively high in the luminance combinations 13, 14, and 15 at CCT 5500 K. The screen luminance of these combinations are all 284.75 cd/m2. Although no statistically significant relationship was found between the cognitive performance of the participants and their comfort, the comfort of the participants tended to decrease after the cognitive operations was completed. According to the proposed personal comfort prediction model, the visual comfort of different people varies even under the same lighting conditions. This study provides a solid theoretical basis for improving the design of lighting environments in enclosed spaces and contributes to developing a pleasant and productive working environment within limited cabins.