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The construction industry is one of the most hazardous industries worldwide, and contact with electricity is a major cause of injury and death among construction workers. It is well known that unsafe acts resulting from human error are the primary cause for up to 80% of accidents across various industries, and some studies show that human performance tools may be functional in mitigating these incidents. Accordingly, this paper provides empirical evidence regarding the effectiveness of human performance tools as used to curb the frequency, probability, and severity of accidents. To achieve its objectives, this study first executed an extensive literature review to identify best practices related to human factors in mitigating the risk of electrical incidents. Then, the authors distributed an online questionnaire among various safety managers to determine the effectiveness of each practice in reducing the frequency, probability and severity of these incidents. The results and analysis show which human performance tools are recognized as most effective in helping safety managers mitigate human errors in electrical jobsites. The results of this study and paper will accelerate and transform current injury-prevention practices as well as overcome some of the barriers in the electrical workplace. An easy-to-use and effective set of human performance best practice solutions will be provided based on standards and industry experience.
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Recognizing Seatbelt-Fastening Behavior with Wearable Technology and Machine Learning
Nearly 1.35 million people are killed in automobile accidents every year, and nearly half of all individuals involved in these accidents were not wearing their seatbelt at the time of the crash. This lack of safety precaution occurs in spite of the numerous safety sensors and warning indicators embedded within modern vehicles. This presents a clear need for more effective methods of encouraging consistent seatbelt use. To that end, this work leverages wearable technology and activity recognition techniques to detect when individuals have buckled their seatbelt. To develop such a system, we collected smartwatch data from 26 different users. From this data, we identified trends which inspired the development of novel features. Using these features, we trained models to identify the motion of fastening a seatbelt in real-time. This model serves as the basis for future work in which systems can provide personalized and effective interventions to ensure seatbelt use.
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- Award ID(s):
- 1952236
- PAR ID:
- 10294626
- Date Published:
- Journal Name:
- Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3411763.3451705
