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. However, there exists no empirically supported intervention for curbing risk habituation. To this end, here we investigated how experiencing an accident in a virtual reality (VR) environment affects workers' risk habituation toward repeatedly exposed workplace hazards. We examined an underlying mechanism of risk habituation at the sensory level and evaluated the effect of the accident intervention through electroencephalography (EEG). The results of pre- and posttreatment analyses indicate experiencing the virtual accident effectively curbs risk habituation at both the behavioral and sensory level. The findings open new vistas for occupational safety training.

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. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty, percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes.

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. However, it is technically complex and costly to design and implement experiments that record multiple measures. To address this issue, a platform that synchronizes multiple measures is needed. METHOD: This paper introduces an open-source platform named OpenSync, which can be used to synchronize numerous measures in neuroscience experiments. This platform helps to automatically integrate, synchronize and record physiological and behavioral signals (e.g., electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, body motion, etc.), user input response (e.g., from mouse, keyboard, joystick, etc.), and task-related information (stimulus markers). In this paper, we explain the features of OpenSync and provide two case studies in PsychoPy and Unity. COMPARISON WITH EXISTING TOOLS: Unlike proprietary systems (e.g., iMotions), OpenSync is free and easy to implement, and it can be used inside any open-source experiment design software (e.g., PsychoPy, OpenSesame, Unity, etc., https://pypi.org/project/OpenSync/ and https://github.com/TAMUCogLab/OpenSync). RESULTS: Our experimental results show that the OpenSync platform is able to synchronize multiple measures with microsecond resolution.

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. 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.