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

项目名称：Xiang， Yujang/Yang， James提案：1700865/1703093目前可用的人工搬运安全评估工具是基于静态起重条件的，例如NIOSH起重方程，它给出了受试者静态起重的最大起重重量，距离和高度。另一种评估手工搬运材料伤害风险的流行方法是测量腰痛引起的腰椎压力。然而，这些方法都是静态的，不能准确反映动态运动的产生和损伤的预防。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，pi将开发一个预测腰椎肌肉骨骼模型，用于动态手动材料处理。该项目旨在开发一种有效的动态起重过程损伤风险评估和预测工具。项目成果将在人工搬运工效学领域具有广泛的应用前景，并将进一步推动起重生物力学的发展。该项目包括本科生和研究生的教育机会。阿拉斯加费尔班克斯大学将与阿拉斯加夏季研究学院合作，为服务不足的少数民族初高中学生举办为期两周的夏季讲习班。另一个为期一周的暑期讲习班将为当地的起重工人和德克萨斯理工大学的西班牙裔中学生提供。该项目侧重于开发一种基于逆动力学优化（预测动力学）的方法，用于使用具有动态强度数据的肌肉骨骼模型进行动态手动材料处理。将开发关键关节的动态强度数据，如膝盖、臀部、脚踝、肘部和下脊柱。该工具用于预测动态起重系统中的伤害事件，将当前的离线程序推进到在线，接近实时的最佳运动控制和伤害预防系统。损伤的定义有两种方式：使用肌肉骨骼模型测量腰椎压缩和剪切应力，以及在关节空间中，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，并通过实验对模型参数进行验证；2)引入腰椎肌肉模型并进行实验验证；3)用非线性规划算法实现这些模型，优化人工搬运过程中的动态升降运动，以实现最小的伤害，并通过实验验证概念。肌肉关节内/关节间耦合将被建模，腰椎区域将被添加，从而产生一个肌肉骨骼模型来测量动态举重过程中背部疼痛的腰椎应力。从活动的人体受试者中估计的关节空间动态强度参数将为模型提供更高的准确性和高效的计算，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。该模型可以近乎实时地计算动态人工物料搬运的时间函数，可用于建立个体特定的动态限制起重过程，以及起重的最佳策略。项目结果将作为考虑动态效果的人工材料搬运人体工程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配方中的各种约束，以及对这些约束的运动适应，将有助于人体运动研究领域的发展，并补充现有的人工物料搬运设计原则。

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.