Integrating Neural Strategies and Biomechanical Output: A Muscle Synergy-Based Computational Framework for Evaluating Human–Passive Wearable Interaction in Industry 5.0

Background/Objectives: Industry 5.0 emphasizes the protection and empowerment of human workers. Passive wearables reduce physical strain, but the evaluation of their efficacy remains incomplete when based solely on kinematics or electromyographic (EMG) envelope amplitude, failing to capture the underlying neural “cost” or the compensatory strategies. This paper proposes a computational framework centered on muscle synergy analysis to bridge the gap between laboratory-grade neural assessment and real-world industrial applications. The goal is to move beyond simple biomechanical metrics toward a deeper understanding of neural coordination during device interaction. Methods: Given the practical limitations of high-density EMG in industrial settings, we propose a “streamlining” approach: laboratory-derived synergy models guide the understanding of neural processes and the selection of a minimal set of sensors capable of detecting maladaptive motor compensations and early signs of fatigue. Results: This approach allows for long-term monitoring without compromising natural movement. By decoupling neural strategies from kinematic output, “silent” risk situations can be identified even when movement appears correct but the neural coordination is altered by the passive device. This supports personalized ergonomic indices and predictive prevention protocols, transforming wearables from simple mechanical aids into intelligent, human-centric systems. Conclusions: This framework provides a roadmap for translating complex motor control theories into practical tools for the next generation of safe and sustainable manufacturing.