Machine Learning, Deep Learning, and Data Preprocessing Techniques for Detecting, Predicting, and Monitoring Stress and Stress-Related Mental Disorders: Scoping Review.

Background: Mental stress and its consequent mental health disorders (MDs) constitute a significant public health issue. With the advent of machine learning (ML), there is potential to harness computational techniques for better understanding and addressing mental stress and MDs. This comprehensive review seeks to elucidate the current ML methodologies used in this domain to pave the way for enhanced detection, prediction, and analysis of mental stress and its subsequent MDs.

Protocol to evaluate the effectiveness of a virtual-reality-based behavioral intervention in enhancing sensory responses to real-world warning.

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.

Virtual accident curb risk habituation in workers by restoring sensory responses to real-world warning.

In high-risk work environments, workers become habituated to hazards they frequently encounter, subsequently underestimating risk and engaging in unsafe behaviors. This phenomenon has been termed "risk habituation" and identified as a vital root cause of fatalities and injuries at workplaces. Providing an effective intervention that curbs workers' risk habituation is critical in preventing occupational injuries and fatalities.

Factors affecting the COVID-19 risk in the US counties: an innovative approach by combining unsupervised and supervised learning.

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level.

OpenSync: An open-source platform for synchronizing multiple measures in neuroscience experiments.

Background: The human mind is multimodal. Most behavioral studies rely on century-old measures such as task accuracy and latency. To better understand human behavior and brain functionality, we need to analyze physiological and behavioral signals of various sources.

An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic.

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.