Collaborative Research: Musculoskeletal Model for Dynamic Manual Material Handling to Prevent Injury

1700865/1703093目前可用的手工搬运安全评估工具是基于静态提升条件的，例如NIOSH提升方程，它给出了受试者静态提升的最大提升重量、距离和高度。评估手工搬运材料的损伤风险的另一种流行方法是测量腰椎背部疼痛的应力。然而，这些方法都是静态的，不能准确地反映动态的运动生成和损伤预防。使用生物力学模型提供了相对较新的替代技术，允许直接测试和个性化结果。在这个合作项目中，PIS将开发一种可预测的腰椎肌肉骨骼模型，用于动态手动处理材料。该项目旨在开发一种有效的工具，用于动态提升过程中的损伤风险评估和预测。项目成果将在人工材料搬运人体工程学方面有潜在的广泛应用，并将进一步推动起重生物力学的发展。该项目包括为本科生和研究生提供教育机会。阿拉斯加大学费尔班克斯大学将与阿拉斯加夏季研究院合作，为服务不足的少数族裔初中生和高中生提供为期两周的暑期研讨会。另一个为期一周的暑期研讨会将在德克萨斯理工大学为当地有起重工作的工人和西班牙裔中学生提供。该项目专注于开发一种基于逆动力学优化(预测动力学)的方法，使用带有动态强度数据的肌肉骨骼模型来动态处理手工材料。将开发关键关节的动态强度数据，如膝盖、臀部、脚踝、肘部和下脊椎。为预测动态起重系统中的伤害事件而开发的工具将把当前的离线过程推进到在线、近乎实时的最佳运动控制和伤害预防系统。损伤有两种定义方式：通过使用肌肉骨骼模型测量腰椎压缩和剪应力，以及在关节间隙，通过测量关键关节的关节扭矩百分比来预测损伤。具体目标是：1)建立通用的动态强度模型并从实验中验证模型参数；2)引入并通过实验验证腰椎肌肉模型；以及3)使用非线性规划算法实现这些模型，以优化手动搬运材料时的动态提升运动，以最大限度地减少损伤，并通过实验演示概念验证。将对肌肉内部/关节间耦合进行建模，并添加腰椎区域，从而生成肌肉骨骼模型，以测量动态提举过程中背部疼痛的腰部应力。由活动性受试者估计的关节间隙动态强度参数将为该模型提供更高的精度和高效的计算，可用于评估复杂的非周期性功能任务的损伤，这些任务可能无法通过传统方法进行实验验证。该模型能够近乎实时地计算作为时间函数的动态人工物料搬运，可用于建立特定于个体的动态极限提升过程，以及提升的最佳策略。项目结果将作为考虑动态影响的人工材料搬运人体工学设计的新指导方针。此外，更好地理解人体生理系统固有的优化公式中的各种约束，以及对这些约束的运动适应，将有助于人类运动研究领域，并补充现有的人工材料处理设计原则。

PIs: Xiang, Yujang/Yang, James Proposals: 1700865/1703093Currently available safety evaluation tools for manual material handling are based on static lifting conditions, such as the NIOSH lifting equation, which gives a subject's maximum lifting weight, distance and height for static lifting. Another popular method for evaluating injury risk for manual material handling is to measure the lumbar spine stress for back pain. However, these methods are all static and cannot accurately reflect dynamic motion generation and injury prevention. Using biomechanical models provides relatively new, alternative techniques that allow direct testing and individualized results. In this collaborative project, the PIs will develop a predictive lumbar spine musculoskeletal model for a dynamic manual material handling. The project aims to develop an efficient tool for injury risk assessment and prediction in dynamic lifting process. Project results will have potentially wide application in manual material handling ergonomics and will further advance lifting biomechanics. The project includes education opportunities for both undergraduate and graduate students. A two-week summer workshop will be offered to underserved minority middle and high school students at University of Alaska Fairbanks in collaboration with the Alaska Summer Research Academy. Another week-long summer workshop will be offered to local workers with lifting jobs and Hispanic middle school students at Texas Tech University.The project focuses on developing an inverse dynamics optimization-based (predictive dynamics) method for dynamic manual material handling using a musculoskeletal model with dynamic strength data. Dynamic strength data will be developed for key joints, such as the knee, hip, ankle, elbow and lower spine. The tool developed for predicting injury events in dynamic lifting systems will advance the current offline procedure to an online, near real-time optimal motion control and injury prevention system. Injury is defined in two ways: by measuring lumbar spine compression and shear stresses using a musculoskeletal model, and, in joint space, by measuring the percentage of joint torque for key joints to predict injury. Specific objectives are to: 1) derive a general dynamic strength model and validate the model parameters from experiments; 2) introduce and experimentally validate a lumbar spine muscle model; and 3) implement these models with a nonlinear programming algorithm to optimize the dynamic lifting motion during manual material handling for minimum injury and experimentally demonstrate proof-of-concept. Muscle intra/inter-joint coupling will be modeled and the lumbar spine area will be added, thereby generating a musculoskeletal model to measure lumbar stresses for back pain in the dynamic lifting process. The dynamic strength parameters in joint space estimated from active human subjects will provide the model with improved accuracy and efficient calculations that can be used to evaluate injury for complex non-periodic functional tasks that may not be experimentally verifiable by traditional means. The model, which enables near real-time calculations of dynamic manual material handling as a function of time, can be used to establish an individual-specific dynamic limiting lifting process, as well as optimal strategy for lifting. Project results will serve as new guidelines for manual material handling ergonomic design, considering dynamic effects. In addition, better understanding of various constraints in optimization formulation inherent in the human physiological system, as well as motion adaptation to these constraints, will contribute to the field of human locomotion study and complement existing design principles for manual material handling.