Gu Sen, Zhang Tianyi, Wang Hanyu, Wang Qingbin
School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China.
School of Design and Art, Henan University of Technology, Zhengzhou 450001, China.
Sensors (Basel). 2025 Aug 14;25(16):5053. doi: 10.3390/s25165053.
Ergonomic assessments for specialized vehicle cockpits are often costly, subjective, or fragmented. To address these issues, this study proposes and validates a quantifiable comprehensive evaluation method combining optical motion capture with simulation. The methodology uses motion capture to acquire accurate, dynamic operator posture data, which drives a digital human model in a virtual environment. A novel assessment framework then integrates the results from six ergonomic tools into a single, comprehensive score using a multi-criteria weighting model, overcoming the 'information silo' problem of traditional software. In a case study optimizing a flatbed transporter cockpit, the method guided a redesign that significantly improved the overall ergonomic score from 0.422 to 0.277. The effectiveness of the optimization was validated by a 40% increase in key control accessibility and a significant reduction in electromyography (EMG) signals in the neck, shoulder, and lumbar regions. This study provides an innovative, data-driven methodology for the objective design and evaluation of customized human-machine systems, demonstrating its utility in reducing physical strain and enhancing operator comfort, with broad applicability to other complex industrial environments.
对专用车辆驾驶舱进行人体工程学评估往往成本高昂、主观或不完整。为了解决这些问题,本研究提出并验证了一种将光学动作捕捉与模拟相结合的可量化综合评估方法。该方法利用动作捕捉获取准确的动态操作员姿势数据,在虚拟环境中驱动数字人体模型。然后,一个新颖的评估框架使用多标准加权模型将六种人体工程学工具的结果整合为一个单一的综合分数,克服了传统软件的“信息孤岛”问题。在一个优化平板运输车辆驾驶舱的案例研究中,该方法指导了一次重新设计,使整体人体工程学分数从0.422显著提高到0.277。关键控制可达性提高40%以及颈部、肩部和腰部区域的肌电图(EMG)信号显著减少,验证了优化的有效性。本研究为定制人机系统的客观设计和评估提供了一种创新的、数据驱动的方法,证明了其在减轻身体疲劳和提高操作员舒适度方面的效用,在其他复杂工业环境中具有广泛的适用性。