Author ORCID Identifier

https://orcid.org/0009-0008-8165-4714

Semester

Fall

Date of Graduation

2023

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Industrial and Managements Systems Engineering

Committee Chair

Jeremy Gouzd

Committee Member

David Wyrick

Committee Member

Avishek Choudhury

Committee Member

John Hando

Committee Member

Ibukun Ogunade

Committee Member

Omar Al-Shebeeb

Abstract

The Centers for Disease Control and Prevention (CDC) emphasized that Personal Protective Equipment (PPE) can significantly reduce the risk of occupational injuries and illnesses. However, improper use, failure to use, and other PPE-related violations can still result in injuries and fatalities. Eye and face protection violation has been one of the top 10 most frequently violated OSHA standards in fiscal years 2018, 2019, 2020, 2021 and 2022 consecutively. A common practice among safety professionals to ensure PPE compliance has been to physically inspect or monitor PPE usage among workers, which has been found to be unsustainable on a continuous real-time basis. While there have been efforts to investigate and recommend solutions to PPE non-compliance over the years, little effort has been made to the concept of continuous real-time PPE compliance monitoring among manufacturing workers.

This dissertation focuses on the use of automated PPE compliance monitoring systems in a manufacturing setting. The traditional method of PPE compliance monitoring, relying on human observation and review of recorded videos, is prone to human errors and limitations. This study aims to investigate the effectiveness of an automated system used in detecting PPE violations in real-time, and to evaluate its scope and limitations under various normal working conditions. Three major hypotheses were proposed, including the impact of worker training or experience on PPE compliance, and a comparison between an automated PPE detection technology (driven by Artificial Intelligence) and video recording with human reviews.

The objective of this study is to provide insights into the effectiveness of an automated PPE compliance monitoring system when compared to traditional PPE monitoring system which relies on human reviews of video recordings, and to inform the development of policies and systems to improve PPE compliance in real-time which can reduce injuries in the manufacturing industry. The findings of this study can contribute to the improvement of workplace safety and health in the manufacturing industry.

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