复杂系统人因与工效学研究所
With the increasing complexity of transportation systems and the growing application of ergonomics in vehicle cab design, digital human models (DHMs) play an increasingly important role in simulating driver posture and optimizing human–machine interaction. However, existing models face limitations in capturing individual differences and dynamic posture variations, which constrain the prediction accuracy of key geometric parameters,such as eye location and upper-limb reachability. This review focuses on driving posture as the core perspective and aims to clarify how posture modeling can improve DHMs to more realistically represent drivers’postural changes during actual operations. By enhancing the fidelity of posture simulation, these improvements contribute to safer and more ergonomic cab designs. We followed the PRISMA guidelines to systematically retrieve literature from the Web of Science and Scopus databases. We found that current research can be broadly categorized into four main themes: (1) collection of anthropometric and joint angles, (2) impact of individual differences on initial driving posture, (3) postural changes during prolonged driving, and (4) geometric interaction modeling between the driver and cab. We proposed a conceptual framework for a DHM-based driving posture representation. This framework defines the inputs and outputs, providing methodological guidance for future research on driving postures in DHMs. As all included studies were from the automotive domain, the framework is evidence-supported for automobiles, while its extension to rail and specialized transport vehicles represents a transferable direction for future research that could ultimately enhance operational safety and human–machine compatibility across broader driving scenarios.
Chengle Fang, Weining Fang*，Wenli Dong，Shengjian Hu, Kun Wang, Ke Niu.Constructing digital human models for ergonomics: a systematic review on driving posture.Safety Science. https://doi.org/10.1016/j.ssci.2026.107398
