RHYTHMS: Real-time Data-driven Human-machine Synchronization for Proactive Ergonomic Risk Mitigation in the Context of Industry 4.0 and Beyond

Abstract

Human-machine work system (HMWS) represents a composite system that integrates machine precision and strength with human cognitive abilities, flexibility, and adaptability, aiming to achieve enhanced performance in various tasks aligned with a common goal, which is getting smarter in the era of Industry 4.0 (I4.0) and has received extensive attention from academia and industry. However, the lack of real-time information sharing during HMWS implementation and operation, coupled with uncertainties stemming from human instability and the complexities introduced by smart networking environments in the context of I4.0, poses challenges to the synchronous coordination of this distributed human-centric manufacturing system. Failure in synchronization not only compromises overall system integrity, leading to unrealistic decision-making and potential harm to human health but also goes against the principles of Industry 5.0 (I5.0), which proposes placing human well-being at the center of the production process for human-centric smart manufacturing in the future. In this context, this research presents real-time data-driven hyper objects orchestrating for the human-machine synchronization (RHYTHMS) based on a service-oriented human-to-machine architecture (SOH2M) and a model reference adaptive fuzzy control for real-time information sharing and synchronous coordination of the smart HMWS. Based on these, a real-life assembly case is carried out by a full-scale HMWS prototype to quantitatively analyze the benefits of the proposed approach in proactive ergonomic risk mitigation (PERM), contributing to the human-centric value-oriented I5.0 era.