Vibe Coding Applications for Industrial Safety

Authors

DOI:

https://doi.org/10.26439/ciii2025.8653

Keywords:

Application, artificial intelligence, industrial, safety, vibe coding

Abstract

This paper explores the practicality of vibe coding for industrial engineering applications. Specifically, it investigates whether vibe coding can be utilized to create a safety-related application. In industrial environments, many occupations involve physical labor or the operation of potentially hazardous machinery, raising significant concerns regarding worker health and safety. Although various tools and applications exist to address these risks, they are often overly generic and may not adequately meet the specific requirements of individual organizations. The advent of artificial intelligence (AI) and machine learning has transformed this landscape. This study demonstrates how vibe coding can be used to develop a simple Android-based application that leverages built-in sensors—such as sound, light, and accelerometers—to monitor workers’ physical activities and track environmental conditions for accident prevention. It is expected that this work will encourage companies engaged in physical labor to develop customized applications aimed at improving worker safety. Beyond this specific prototype, the proposed approach illustrates how AI-driven coding can bridge the gap between generic safety solutions and tailored workplace applications.

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Author Biographies

  • Michael Dorin, School of Software Engineering and Data Science, University of St. Thomas, St. Paul, MN, USA

    Michael Dorin holds a PhD in Computer Science from Universität Würzburg, Germany, with a specialization in software readability and quality. He is currently an Assistant Professor in Software Engineering and Data Science at the University of St. Thomas and previously served as a professor at Metropolitan State University in St. Paul, Minnesota. He has more than three decades of professional experience in software development across diverse engineering environments, with a focus on public safety communications and medical devices, including pacemakers, telephony systems, and aircraft navigation technologies. Earlier in his career, he contributed to the initial deployment of worldwide email and document exchange systems at IBM. In addition to his academic career, he founded and managed EDI Enterprises, a telecommunications products company. His recent publications include work on genetic algorithms for e-waste mitigation (2025), machine learning applications for endangered language restoration (2025), ethical considerations in software engineering (2021), and linguistic economy in programming identifiers (2021). His research and professional interests focus on software readability and quality, natural language processing applications in software engineering, AI-assisted programming education, and endangered language preservation and documentation.

  • Juan M. Machuca De Pina, Carrera de Ingeniería Industrial, Universidad de Lima, Perú

    Juan Manuel Machuca holds a Master of Science degree in Teaching and Management from Universidad Marcelino Champagnat and a degree in Industrial Engineering from Universidad de Lima, Peru. He is currently a Professor and Researcher at Universidad de Lima and Universidad del Pacífico, where he is a member of the Industry 4.0 and Applied Analytics Research Group. Throughout his career, he has carried out extensive consulting activities in logistics, planning, and commercial information systems. His recent publications include work on goal programming for vehicle routing in megacity beverage distribution (2025), lean management practices in retail (2023), virtual learning satisfaction in engineering education (2021), and humanitarian logistics optimization under uncertainty (2019). His research has demonstrated practical impact in solving complex real-world problems, particularly in Peru’s urban contexts. His areas of expertise include operations research, humanitarian logistics, vehicle routing optimization, disaster management, lean management practices, and emerging technologies, including digital twins and machine learning applications in warehouse management. His work contributes to sustainable development goals, particularly the creation of sustainable cities and communities through improved logistics and supply chain management systems.

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Published

2026-06-08

Issue

No. 004 (2025): IV Congreso Internacional de Ingeniería Industrial

Section

Ponencias

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