Human-Centered Manufacturing

Description

This research area is situated within the Industry 5.0 vision and aims to develop methods and tools capable of understanding, measuring, and systematically integrating the human dimension into production systems. Focus is specifically directed toward the physical and cognitive well-being of the operator, viewed as a key element for the sustainability, quality, and resilience of industrial processes.

• Objective and predictive ergonomic assessment. Development of approaches to overcome the limitations of traditional methodologies through the integrated use of wearable sensors, inertial systems, computer vision, and electromyography. The application of Artificial Intelligence and Machine Learning algorithms enables continuous monitoring in real-world contexts, facilitating the transition from descriptive analysis to interpretative and predictive models. The goal is the recognition of risk patterns and the management of inter-individual variability to support workplace design based on objective data and personalized assessments.
• Monitoring of cccupational stress and cognitive load. Development of objective, data-driven methods for assessing mental well-being in advanced industrial settings. By analyzing physiological, behavioral, and environmental signals — and employing machine learning algorithms — this research aims to identify latent patterns of fatigue and work-related stress. The objective is to create intelligent systems capable of adapting to individual variability and supporting work organization in the proactive prevention of cognitive overload, ensuring high standards of safety, quality, and sustainability.
• Design and simulation of human-machine interaction. Development of methodologies based on Digital Human Modeling (DHM) for the design, analysis, and optimization of workstations. The research addresses industrial concept design by integrating risk analysis, motion planning and simulation, and dynamic task allocation between the human and the system. Attention is also given to the study of adaptive interfaces and user-centered interaction logic capable of responding to operator needs in real time. The goal is to create work environments that guarantee safe interaction and high standards of operational efficiency and user experience.
• Extended Reality for production process optimization. Integration of Virtual (VR), Augmented (AR), and Mixed Reality (MR) for the design, management, and support of operations. The research utilizes motion capture systems and haptic interfaces for immersive design reviews of processes, ensuring performance optimization while meeting ergonomics and safety requirements. The use of AR/MR solutions supports the operator through the development of:
  • Step-by-step digital instructions to reduce errors and mental workload.
  • Advanced training solutions for rapid and effective learning.
  • Hands-free remote assistance systems.

Publications

• Ciccarelli, M., Papetti, A., Germani, M., 2025. Empowering industry 5.0: automated sensor-based ergonomic risk assessment. International Journal on Interactive Design and Manufacturing 19, 7731–7753. https://doi.org/10.1007/s12008-025-02412-5
• Ciccarelli, M., Germani, M., Marinelli, F., Papetti, A., Pizzuti, A., 2026. An ergonomic zone polyhedral representation-based mathematical program to prevent work-Related musculoskeletal risks. Expert Systems With Applications 302, 130579. https://doi.org/10.1016/j.eswa.2025.130579
• Cruciata, L., Contino, S., Ciccarelli, M., Pirrone, R., Mostarda, L., Papetti, A., Piangerelli, M., 2025. Lightweight Vision Transformer for Frame-Level Ergonomic Posture Classification in Industrial Workflows. Sensors 25, 4750. https://doi.org/10.3390/s25154750
• Ciccarelli, M., Forlini, M., Papetti, A., Palmieri, G., Germani, M., 2024. Advancing human–robot collaboration in handcrafted manufacturing: cobot-assisted polishing design boosted by virtual reality and human-in-the-loop. International Journal of Advanced Manufacturing Technology 132, 4489–4504. https://doi.org/10.1007/s00170-024-13639-z
• Rescio, G., Manni, A., Caroppo, A., Ciccarelli, M., Papetti, A., Leone, A., 2023. Ambient and wearable system for workers’ stress evaluation. Computers in Industry 148, 103905. https://doi.org/10.1016/j.compind.2023.103905

Staff

Prof.ssa Alessandra Papetti
Tel. +39 071 220 4880
E-mail: a.papetti@staff.univpm.it