Human-Machine Interfaces in Automotive Ergonomic Manufacturing: A Review

This systematic review examines ergonomic human-machine interfaces in automotive manufacturing to identify strategies for improving operator well-being and productivity. This review examined the application of ergonomic design, compared cognitive load-management methods, safety enhancement strategies, analyzed adaptive interface technologies, and evaluated models of interaction between operators and equipment. A methodological review of various research projects that utilised virtual and augmented reality, physiological tracking, machine learning, and digital twin technologies in the context of automotive manufacturing was conducted. Individualised ergonomic solutions and virtual simulations are effective in preventing musculoskeletal hazards and improving operator comfort and productivity, the results indicate. AI-based cognitive load management systems using physiological data reduce mental workload and improve decision-making. Adaptive human-robot safety strategies using real-time risk assessment increase operator trust and reduce accidents. Operability capabilities-based interface and task difficulty interface support usability gains and reduced errors, and user-equipment interaction paradigms support increased collaboration and performance. Overall, these results highlight the promise of ergonomic, cognitive, and safety synergies embedded in adaptive technology to streamline human-machine interfaces. The review points to more realistic validation and interdisciplinary strategies to develop scalable human-friendly manufacturing systems that strike a balance between efficiency, safety, and the health and safety of the operator.