Construction workers’ emotional states (e.g., pleasure, displeasure, excitement, and relaxation) are known as a critical factor that affect their performance (e.g., safety, health, and productivity). To prevent adverse impacts on work performance, measuring emotional states should take precedence to better understand how workers’ emotions vary while they are working. Among many methods available to measure emotional states, electroencephalogram (EEG) has a great potential for quantitative measurement by overcoming a possible bias from the survey-based subjective assessment of emotions. Although EEG-based emotion measurement has been tested and applied only in a laboratory environment, recent advancements in wearable EEG sensors, which are portable, wireless, and affordable, open a new door toward nonintrusive field emotion measurement. This study thus investigates the feasibility of measuring workers’ emotions in the field using a wearable EEG sensor. To do this, a bipolar dimensional emotion model, which consists of valence (from displeasure to pleasure) and arousal (from relaxation to excitement) dimensions, was applied to quantify workers’ emotional states. Then, workers’ valence and arousal levels were measured using a wearable EEG sensor during their ongoing tasks. The validity of the EEG-based emotion measurement was examined through a comparison with cortisol levels obtained from workers’ saliva samples, which has been accepted as a reliable physical measure of emotions. The results demonstrate the applicability of a wearable EEG sensor for measuring workers’ emotions, particularly valence levels, which remain crucial to understanding workers’ emotional states. This study contributes to the body of knowledge on in-depth studies for understanding workers’ emotions in the field by providing a means to continuously and nonintrusively measure workers’ emotions while they are working.

Investigating brain waves collected by an electroencephalogram (EEG) can be useful in understanding human psychosocial conditions such as stress, emotional exhaustion, burnout, and mental fatigue. Recently, an off-the-shelf wearable EEG device, which is wireless, lightweight, and affordable, has become available so that field construction workers’ psychosocial status can be explored without interfering with their ongoing work. However, capturing high-quality EEG signals from such a device can be very challenging at real construction sites because of the signal artifacts generated by body movement caused by physically demanding work. To address this issue, the authors propose an EEG signal-processing framework that can acquire high-quality EEG signals at real construction sites using a wearable EEG device. Specifically, the signal-processing framework reduces noises and is thus able to extract quality EEG signals. This framework is validated by examining whether brain activation (particularly by body movements) can be identified using the processed EEG signal applied to eight field construction workers under working (i.e., active) and not working (i.e., inactive) conditions. Specifically, mean power spectral density (PSD) of the EEG beta frequency range is calculated from electrodes near the motor cortex, the part of the brain that controls voluntary movements. A significant difference in mean PSD in the beta frequency range between active and inactive conditions demonstrates that the processed EEG signal, based on the proposed framework, captures brain activation. The results show the potential of the proposed signal-processing framework to monitor workers’ brain wave patterns in the field with a wearable EEG device, opening up an opportunity to assess workers’ psychosocial status in construction so that any psychosocial problems of workers can be investigated.

Construction is one of the most dangerous occupations. The most prominent reason for worker accidents is unsafe behavior, particularly rooted in the inability to recognize and appropriately respond to risk factors. Previous studies have investigated worker perception and comprehension of construction hazards and safety information in laboratory settings. A relationship has been found between workload and workers’ situation awareness (SA), a useful concept for understanding risk perception and comprehension. However, it is necessary to investigate the actual construction environment to increase the real-world applicability of the research results. This experimental study analyzed workers’ SA in relation to physical and mental workloads acquired while performing work at a construction site. Perception (Level-1-SA) and comprehension (Level-2-SA) were measured using the SA Global Assessment Technique as subjects walked inside a facility under construction and performed inspection or construction works. Statistically significant differences were found between perception and comprehension of safety-related information. It was also found that mental load negatively affected SA. This study provided external validation of previously investigated effects of workload on workers’ perception and comprehension of safety-related elements at real construction sites. The study’s findings can improve the understanding of workers’ safety behaviors and help suggest directions for safety management guidelines. Graphical Abstract

Urban policies have recently been formulated, following the increasing interest in pedestrian-friendly cities, people-centered safety, and accessibility. Despite the research efforts on physical walking safety, safety evaluations centered on pedestrian perception have been under-reported. Investigating the factors affecting pedestrian subjective safety perception is critical to promoting walking intention because pedestrians forgo walking if they feel unsafe. This study explored the relationship between various walking environmental factors and pedestrians’ psychological perception of safety by surveying 99 pedestrians’ perceptions at nine study sites and conducting a field investigation. Because of the multifaceted nature of pedestrian perception, mediation effect analyses were also conducted to understand the relationship between walking environment factors and perceived safety in depth, considering the role of the perception of traffic characteristics and walking infrastructure. This study found that walking environmental factors closely related to physical safety (e.g., traffic safety facilities and crosswalks) may not greatly contribute to perceived safety and demonstrated that maintaining infrastructure quality is essential for enhancing perceived safety, considering the mediating effect of the perception of infrastructure on perceived safety. The results imply that to improve the walking environment, it is necessary to consider both the physical safety and the perceived safety of pedestrians. This requires comprehensive planning for enhancing traffic safety facilities as well as ensuring user comfort and pleasure through quality infrastructure. This study can provide a basis for enhancing pedestrian-centered safety and promoting residents’ walking intention for public health while increasing their perceptions of safety.

Construction is known as one of the most stressful occupations due to its involvement with physiologically and psychologically demanding tasks performed in a hazardous work environment. Because workers’ stress is a critical factor that adversely affects workers’ productivity, safety, well-being, and work quality, understanding workers’ stress should take precedence in the management of excessive stress. Various instruments for subjective measurement towards one’s perceived stress have been used, but such methods rely on imprecise memory and reconstruction of feelings in the past. Recent advancements in wearable Electroencephalography (EEG) devices possess a potential for quantitative measurement of human stress by directly capturing central nervous system activities from stress. However, its capability of measuring field workers’ stress under real occupational stressors remains questionable. This research thus proposes a framework to recognize construction workers’ stress at the field based on their brain activity recorded from a wearable EEG. Specifically, this framework applies a supervised learning algorithm—support vector machine—in detecting workers’ stress while working in different conditions. Workers salivary cortisol levels, which is a stress-related hormone, were used to label the tasks as low or high-stress level. Relevant time and frequency domain features in EEG signals were calculated. Results yielded a high of 71.1% accuracy using SVM in recognizing workers’ stress, which is a very promising result given that stress recognition with an exquisite EEG device in the clinical domain has at most the similar level of accuracy. The results show the potential for recognizing construction workers’ stress at the field by applying machine learning algorithms using workers’ brain waves recorded from a wearable EEG device. This EEG based stress detection approach will help to enhance workplace environment and conditions as well as to improve workers’ health by early detection and mitigation of the factors that cause stress.

The mobility impaired, the deaf or hard of hearing, the blind or visually impaired, the cognitively impaired, and the elderly population are among several examples of groups categorized as particularly vulnerable to fire-related hazards. Given the severity of the threat that building fires pose for these vulnerable groups in terms of the different types of debilitation they experience, it is crucial to distinguish the respective attributes of each group and map out how such differences lead to differing performance levels during fire evacuations. To better gauge each group’s capacity to cope with building fires, this study collected survey data from social service providers and staff members who provide care for vulnerable healthcare facility residents. The questionnaires were designed to assess each group’s emergency response abilities and render them in quantifiable form in terms of perception, interpretation, decision-making, and mobility. The results of the survey serve as the input values for an evacuation simulation model which analyzes the evacuation performances (i.e., response and movement time) of vulnerable groups. The study concludes by proposing managerial strategies for the enhancement of fire safety in healthcare facilities on the basis of outcome analysis. Understanding the evacuation characteristics of disabled and vulnerable groups is expected to provide a foundation for the safety managers and staff members of relevant facilities to prepare for and deal with unexpected emergencies in an efficient and effective manner.