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

合作研究：用于动态手动物料搬运以防止受伤的肌肉骨骼模型

基本信息

批准号：
1849279
负责人：
Yujiang Xiang
金额：
$ 28.74万
依托单位：
Oklahoma State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-30 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849279&HistoricalAwards=false
关键词：
Collaborative Research Musculoskeletal Model Dynamic

项目摘要

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.

PI：Xiang、Yujang/Yang、James 提案：1700865/1703093 目前用于手动物料搬运的安全评估工具都是基于静态提升条件，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。评估手动物料搬运受伤风险的另一种流行方法是测量背痛时的腰椎压力。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预防。使用生物力学模型提供了相对较新的替代技术，可以直接进行测试并获得个性化结果。在这个合作项目中，PI 将开发一个用于动态手动物料搬运的预测性腰椎肌肉骨骼模型。该项目旨在开发一种有效的工具，用于动态举升过程中的伤害风险评估和预测。项目结果将在手动物料搬运人体工程学方面具有潜在的广泛应用，并将进一步推进提升生物力学。该项目包括本科生和研究生的教育机会。阿拉斯加大学费尔班克斯分校将与阿拉斯加夏季研究学院合作，为服务不足的少数族裔初中和高中学生提供为期两周的夏季研讨会。另一个为期一周的夏季研讨会将为从事起重工作的当地工人和德克萨斯理工大学的西班牙裔中学生提供。该项目的重点是开发一种基于逆动力学优化（预测动力学）的方法，使用具有动态强度数据的肌肉骨骼模型进行动态手动物料搬运。将为膝、髋、踝、肘和下脊柱等关键关节开发动态力量数据。为预测动态提升系统中的伤害事件而开发的工具将把当前的离线程序推进到在线、近实时的最佳运动控制和伤害预防系统。损伤有两种定义：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）推导出通用的动态强度模型并通过实验验证模型参数； 2）引入并实验验证腰椎肌肉模型； 3) 使用非线性编程算法实现这些模型，以优化手动物料搬运期间的动态提升运动，以最大限度地减少伤害，并通过实验证明概念验证。对肌肉内/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来测量动态举升过程中背痛的腰椎应力。从活跃的人类受试者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有效的计算，可用于评估复杂的非周期性功能任务的损伤，而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动态手动物料搬运随时间的变化，可用于建立特定于个人的动态限制提升过程以及最佳提升策略。项目结果将作为考虑动态效应的手动物料搬运人体工程学设计的新指南。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，并补充现有的手动物料搬运设计原则。