Enhancing Worker Safety in Manufacturing with IoT and ML

Abstract

This paper explores the integration of Internet of Things (IoT) and Machine Learning (ML) technologies to enhance worker safety in manufacturing environments. Through a comprehensive review of existing literature, the paper investigates the impact of IoT and ML on various aspects of worker safety, including accident prevention, injury reduction, and productivity improvement. Quantitative analysis reveals significant reductions in accident rates and severity of injuries, alongside improvements in productivity, attributable to IoT and ML interventions. Moreover, comparisons with traditional safety measures highlight the advantages of proactive risk management and real-time monitoring enabled by IoT and ML. Ethical considerations and social implications are also discussed, emphasizing the importance of data privacy, algorithmic fairness, and stakeholder engagement. Recommendations for industry practitioners and policymakers include investing in technology infrastructure, prioritizing data privacy and security, and fostering a culture of safety. Future research directions encompass exploring ethical and social implications, assessing long-term impact, and embracing emerging technologies such as edge computing and federated learning. Overall, this paper underscores the transformative potential of IoT and ML in creating safer, more resilient workplaces.