Enhanced Multi-Objective Biomechanical Optimization with Hybrid Genetic Algorithm for Realistic Manual Lifting Postures

Abstract

Manual material handling (MMH) is a major contributor to work-related low back disorders (LBDs). At the same time, traditional ergonomic tools—such as the NIOSH lifting equation—offer limited guidance due to their single-objective nature. This study proposes an enhanced multi-objective optimization framework that integrates biomechanical loading, metabolic efficiency, and postural stability to identify safer and more practical lifting postures. An observational cross-sectional study involving 34 metal-manufacturing workers was conducted to capture lifting kinematics, electromyography, and force-plate data. A hybrid optimization algorithm combining NSGA-II and pattern search was implemented to model L5/S1 compression, shear forces, metabolic cost, and dynamic stability within a unified computational platform. The optimization produced a Pareto front of 47 non-dominated solutions. The selected compromise posture reduced L5/S1 compression by 28.1% (2509.9 N vs. 3485.4 N for stooped technique) and shear force by 31.2% (812.4 N vs. 1178.2 N), while improving dynamic stability by 11.5% (0.87 vs. 0.78). These benefits were achieved with a manageable 15.1% increase in metabolic cost (285.7 J vs. 248.3 J). Field evaluation showed substantially higher worker acceptance (80%) compared to conventional squat lifting (45%), accompanied by a reduction in perceived exertion (Borg score: 15.7 to 12.3). The proposed framework offers a robust, practical approach to designing evidence-based ergonomic interventions to mitigate spinal risks associated with lifting.