Human-Centered optimization through Digital Twins, and Motion Capture Technologies of a manual activity in the logistics sector

Industry 4.0 has enabled significant technological advances for industrial applications, creating new business opportunities but often neglecting how operators interact with increasingly complex systems. In contrast, Industry 5.0 emphasizes a human-cantered approach, highlighting the role of operators and their interaction with automation in modern industrial environments. Despite technological progress, many industrial sectors, such as logistics, continue to rely on fully manual tasks. These workstations are frequently poorly optimized, ergonomically inadequate, and not inclusive of diverse operators. The repetitive and physically demanding nature of such tasks can lead to fatigue, stress, and increased risk of injury, negatively impacting both operator well-being and productivity.This paper proposes an innovative methodology to optimize manual operations in the logistics sector through advanced technologies and a human-centered design approach. The goal is to enhance inclusivity, reduce physical loads, and minimize injury risks associated with repetitive or hazardous activities. To this end, motion capture systems (MoCap) and digital human simulation software were employed to develop a digital twin of both operators and workstations. This virtual model enabled the analysis of the current situation and the simulation of multiple optimization scenarios. By using this risk-free environment, alternative automation solutions were evaluated, and the most effective configurations were identified based on performance, efficiency, and safety.A comprehensive ergonomic evaluation complemented the analysis, assessing key indicators to define the optimal task distribution between human operators and automated systems. This ensured minimized physical load on operators while maximizing operational efficiency. Virtual reality (VR) technology was integrated into the validation process, allowing operators to interact directly with proposed solutions in a virtual setting. The proposed methodology was applied to a real logistics process to validate its practicality and effectiveness. Preliminary results confirmed its potential in industrial applications, demonstrating improvements in ergonomics, inclusivity, and productivity. These findings are further detailed and discussed in the final version of the paper.Additionally, a dedicated study was conducted on the number of sensors required in MoCap acquisitions. The objective was to determine the minimum number of sensors necessary to accurately reproduce operator motion, while exploiting the posture prediction capabilities of a digital human simulation software IPS IMMA. This analysis is important due to the fact that reducing the number of sensors directly lowers acquisition time, system complexity, and implementation costs, thereby making the methodology more practical and scalable for industrial deployment. By identifying an optimal compromise between sensor quantity and motion fidelity, the study contributes to the efficient and sustainable use of advanced motion capture technologies in industrial contexts.Overall, this work highlights the importance of integrating ergonomic considerations and human factors into industrial automation strategies. By placing the operator at the center of system design, the study demonstrates how logistics operations can be optimized not only for efficiency but also for inclusivity, safety, and operator well-being. These findings provide practical insights for the transition toward Industry 5.0, where human–machine collaboration is essential for sustainable productivity and improved job satisfaction.