Given the dynamic and complex nature of the construction industry, maintaining situation awareness at job sites is critical. To react properly, workers must identify dynamic safety hazards within the scene. The majority of studies assessing construction workers’ situation awareness have utilized static images, virtual reality, and other types of simulation methods, but questions remain as to whether these formats are able to capture and monitor workers’ naturalistic behaviors and hazard identification abilities. To identify whether the format of hazardous stimuli (i.e., static, image-based vs. dynamic, and video-based formats) impact workers’ subjective and objective hazard identification and situation awareness metrics, this study developed 23 safety hazard scenarios utilizing state-of-the-art augmented 360° panoramas and then tracked differences in workers’ visual search patterns and hazard identification abilities using eye-tracking technology. The workers’ cognitive responses, evidenced by their eye movements, showed that workers had significantly varied cognitive processes and abilities depending on the format of stimuli: Workers with lower hazard identification skills were more likely to miss hazards in a dynamic environment. This result suggests that the experimental setting should be carefully designed to determine construction workers’ natural cognitive process.
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Anshimi: Women's Perceptions of Safety Data and the Efficacy of a Safety Application in Seoul
- Award ID(s):
- 1901367
- NSF-PAR ID:
- 10284322
- Date Published:
- Journal Name:
- Proceedings of the ACM on Human-Computer Interaction
- Volume:
- 5
- Issue:
- CSCW1
- ISSN:
- 2573-0142
- Page Range / eLocation ID:
- 1 to 21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)Chemical Safety Data Sheets (SDS) are the primary method by which chemical manufacturers communicate the ingredients and hazards of their products to the public. These SDSs are used for a wide variety of purposes ranging from environmental calculations to occupational health assessments to emergency response measures. Although a few companies have provided direct digital data transfer platforms using xml or equivalent schemata, the vast majority of chemical ingredient and hazard communication to product users still occurs through the use of millions of PDF documents that are largely loaded through manual data entry into downstream user databases. This research focuses on the reverse engineering of SDS document types to adapt to various layouts and the harnessing of meta-algorithmic and neural network approaches to provide a means of moving industrial institutions towards a digital universal SDS processing methodology. The complexities of SDS documents including the lack of format standardization, text and image combinations, and multi-lingual translation needs, combined, limit the accuracy and precision of optical character recognition tools. The approach in this document is to translate entire SDSs from thousands of chemical vendors, each with distinct formatting, to machine-encoded text with a high degree of accuracy and precision. Then the system will "read" and assess these documents as a human would; that is, ensuring that the documents are compliant, determining whether chemical formulations have changed, ensuring reported values are within expected thresholds, and comparing them to similar products for more environmentally friendly alternatives.more » « less
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3449221
