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<p>Musculoskeletal disorders (MSDs) are among the leading causes of workplace injuries and reduced productivity. Conventional ergonomic assessments often rely on subjective observation, limiting accuracy and prevention. There is a clear gap in adopting computational biomechanics tools that can provide objective, predictive, and scalable solutions for workplace ergonomics. This review synthesizes literature on three key approaches—digital human modeling (DHM), finite element analysis (FEA), and AI-driven simulations—evaluating their theoretical foundations, practical applications, and limitations in posture assessment and workplace safety. DHM supports proactive ergonomic design through virtual worker–task interaction; FEA enables detailed strain and tissue-level analysis for injury risk prediction; AI offers scalability for real-time monitoring and early risk detection. However, widespread implementation is constrained by high computational demands (FEA), limited standardization (DHM), and data dependency (AI). To advance adoption, this paper proposes a roadmap emphasizing cost-effective simulation platforms, collaborative validation frameworks, and integration of AI with industry-ready DHM and FEA tools. These strategies can accelerate translation from research to practice, improving ergonomic design and reducing MSD prevalence in workplaces.</p>

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Computational Biomechanics for Ergonomic Workplace Design: A Review

  • 期刊论文

  • 发表日期：

    2026年1月7日

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  • Adinife Patrick Azodo

摘要

Musculoskeletal disorders (MSDs) are among the leading causes of workplace injuries and reduced productivity. Conventional ergonomic assessments often rely on subjective observation, limiting accuracy and prevention. There is a clear gap in adopting computational biomechanics tools that can provide objective, predictive, and scalable solutions for workplace ergonomics. This review synthesizes literature on three key approaches—digital human modeling (DHM), finite element analysis (FEA), and AI-driven simulations—evaluating their theoretical foundations, practical applications, and limitations in posture assessment and workplace safety. DHM supports proactive ergonomic design through virtual worker–task interaction; FEA enables detailed strain and tissue-level analysis for injury risk prediction; AI offers scalability for real-time monitoring and early risk detection. However, widespread implementation is constrained by high computational demands (FEA), limited standardization (DHM), and data dependency (AI). To advance adoption, this paper proposes a roadmap emphasizing cost-effective simulation platforms, collaborative validation frameworks, and integration of AI with industry-ready DHM and FEA tools. These strategies can accelerate translation from research to practice, improving ergonomic design and reducing MSD prevalence in workplaces.