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Construction jobs are more labor-intensive compared to other industries. As such, construction workers are often required to exceed their natural physical capability to cope with the increasing complexity and challenges in this industry. Over long periods of time, this sustained physical labor causes bodily injuries to the workers which in turn, conveys huge losses to the industry in terms of money, time, and productivity. Various safety and health organizations have established rules and regulations that limit the amount and intensity of workers' physical movements to mitigate work-related bodily injuries. A precursor to enforcing and implementing such regulations and improving the ergonomics conditions on the jobsite is to identify physical risks associated with a particular task. Manually assessing a field activity to identify the ergonomic risks is not trivial and often requires extra effort which may render it to be challenging if not impossible. In this paper, a low-cost ubiquitous approach is presented and validated which deploys built-in smartphone sensors to unobtrusively monitor workers' bodily postures and autonomously identify potential work-related ergonomic risks. Results indicates that measurements of trunk and shoulder flexions of a worker by smartphone sensory data are very close to corresponding measurements by observation. The proposed method is applicable for workers in various occupations who are exposed to WMSDs due to awkward postures. Examples include, but are not limited to industry laborers, carpenters, welders, farmers, health assistants, teachers, and office workers.

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The novel SARS-CoV-2 coronavirus caused a global pandemic in 2020 with millions of diagnosed cases and a staggering number of deaths. As a preventive measure, many governments issued social distancing and shelter-in-place mandates to limit human contact and slow the rate of infection. The large extent and duration of the crisis is poised to transform the health sector and alter current practices in retail, business, manufacturing, and construction. While medical researchers are working on antidote and vaccine solutions, contact tracing and selfisolation are deemed effective methods to control community spread. This paper presents a visual analysis approach that uses convolutional neural networks (CNNs) to generate quantifiable metrics of contact tracing. In particular, the YOLO-v3 architecture was trained on an annotated video dataset containing pedestrians. Network pruning and non-maximum suppression were applied to optimize model performance, resulting in 69.41% average precision. The fully trained model was then tested on sample crosswalk video data from Xiamen, China, recorded during the COVID-19 pandemic, followed by projecting detected pedestrians onto an orthogonal map for contact tracing by tracking movement trajectories and simulating the spread of droplets among the healthy population. Results demonstrate that the proposed technique is capable of tracing and documenting infection sources, times, and locations.