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Anxiety disorder is the most common mental health disorder in the United States. One of the key factors that leads to the development and aggravation of anxiety disorders is mental stress. In this study, we reviewed publications that used physiological responses and symptoms to assess mental stress. This review found that mental stress affects heart rate, hear rate variability, blood pressure, and skin conductance. Fuzzy logic, time series, and Poincare plots are prominent data analysis tools for physiological data. Most studies used a threshold (e.g., Poincaré plot) or variance (e.g., moving average models) to distinguish stress from normal conditions. The variations and thresholds, however, might fluctuate across various activities and individuals. Moreover, most research evaluated lab-generated stress data, which may be biased. Therefore, more naturalistic studies should be conducted for future research.

The goal of this paper is to review the literature on machine learning (ML) and big data applications for mental health, emphasizing current research and practical implementations. To explore the field of ML in mental health, we used a scoping review process. The literature identified application domains of detection and prediction of stress as a contributor to mental health disorders. We evaluated the articles and data on the mental health application, machine learning approach, type of data (sensor, survey, etc.), and type of sensors. Most studies extracted features before developing AI-based stress detection algorithms. Findings revealed that heart rate, heart rate variability, and skin conductance features are the key indicators of stress. Moreover, among AI stress-detection methods, Random Forest and Neural Networks show promising results.

Habituation to signals that warn of a potential danger in high-risk work environments is a critical causal factor of workplace accidents. Such habituation is hard to measure in a real-world setting, and no existing intervention can effectively curb it. Here, we present a protocol to enhance workers’ sensory responses to frequently encountered warnings at workplaces using a virtual-reality-based behavioral intervention. We describe steps for performing a virtual reality experiment and an electroencephalography (EEG) experiment with human participants.

The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people’s routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus’s spread. In this paper, we developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, we collected and annotated 1000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1853 images and increased the dataset to 3300 images by data augmentation. Then, we trained and tested multiple Tensorflow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, we employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle’s effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. We also used transfer learning for training the model. The final model was applied on four videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers’ safety in the pandemic situation.