• Nuclear industry
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• v.36 no.11
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• pp.12-18
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• 2016

• Safety and Health at Work
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• v.11 no.4
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• pp.537-542
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• 2020

Background: Personal protective equipment (PPE) has been designed in such a way to reduce accident rates. Unfortunately, existing PPE is rather ineffective as it is not able to provide warning signals when hazard is around. The integration of intelligent systems is envisaged to increase the efficiency of existing PPE. Methods: This project designed a safety vest incorporated with metal detectors which can provide immediate warning to the field workers when there is metal hazard around. This product has greater freedom of design via smart manufacturing as it involves the assembly of few commercially available parts into a single entity. Briefly, the metal detector is a do it yourself (DIY) kit, and the safety vest is purchasable from any local market. The DIY kit was connected to a copper coil and being sewed into the safety vest. Results: The metal detector induces beeping sound when there is metal hazard around. A total of 121 engineering students were introduced to the prototype before being requested to answer a survey associated with the design. Respondents have rated >3.00/5.00 for the design simplicity, ease of usage, and light weight. Meanwhile, respondents suggested that the design should be further improved by increasing the metal detection range. Conclusion: It is envisaged that the introduction of this smart safety vest will allow the workers to carry out their duties securely by reducing the accident rates. Particularly, such design is expected to reduce workplace accident especially during night time at construction sites where the visibility is low.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.

• Journal of the Society of Disaster Information
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• v.12 no.4
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• pp.381-388
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• 2016

In this study, we surveyed the supply and perception of Personal Protective Equipments for firefighters. As a result, most of the respondents answered that they were paid in accordance with the supply standard, but 12% of the respondents said that there were insufficient equipment. Also, satisfaction with the performance of the equipment is mostly satisfied, but the safety gloves are more than 50% dissatisfied. In summing up the results of the survey, it is urgently necessary to improve the current supply standard, which is determined by the life of PPE, to the replacement period according to the using time or frequency. Also, a new standard should be prepared through the follow - up study.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.253-259
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• 2022

This study aims to investigate the antecedent of consumer personal preventive equipment purchase behaviour by extending the HBM model after the pandemic. Pandemic related studies have focused on the effect of perceived susceptibility and perceived severity on consumer preventive behaviour, little studies have investigated the antecedents of consumer perceived risk. This study filled the gaps in the previous studies. This study tested all proposed hypotheses among users who have purchase the self-preventive behaviour. In final 253 valid data were collected through online survey for statistics analysis. This study found that consumer's health consciousness significantly impacted consumer's perceived severity of COVID-19 and perceived risk. Perceived risk positively impacted consumer self-preventive equipment purchase intention. In contrast, perceived susceptibility did not significantly consumer perceived risk. Based on these results, the theoretical implication will be offered on the study of health-related studies and will be given insight for disease control center to effectively manage consumer self-preventive behaviour.