Explaining Ergonomic Dynamics via Robust Syntropy

Musculoskeletal injuries constitute a critical industrial challenge, yet predictive ergonomic methodologies have remained largely stagnant. Conventional assessments are primarily limited to static, linear snapshots of posture, resulting in a failure to capture the inherently dynamic and chaotic nature of human movement over time. To address this limitation, the present study introduces an innovative approach grounded in nonlinear dynamical systems and robust statistics. The objective was to determine whether information-theoretic and dynamic metrics, specifically Shannon Entropy and the Largest Lyapunov Exponent (LyE), can accurately quantify the structural stability of the cervical spine during a fatigue-inducing isometric protocol. To mitigate the inherent noise of field-derived biomechanical data and prevent algorithmic singularities during phase-space reconstruction, zero-phase Butterworth filtering as well as uniform Gaussian noise injection (Gaussian dither) were systematically applied. The analysis revealed a clear, quantifiable topological shift: as localized muscular fatigue accumulates, the biomechanical system abruptly transitions from a state of stable, organized kinematic flow directly into chaotic instability. Furthermore, these interpretable nonlinear metrics demonstrate high suitability for integration into Explainable Artificial Intelligence (XAI) safety architectures. By transforming complex, stochastic biomechanical data into clear diagnostic biomarkers, this research establishes a foundational framework for proactive, real-time preventive safety measures, thereby advancing the paradigm of Syntropic Bioergonomics.