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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：
1700865
负责人：
Yujiang Xiang
金额：
$ 30.92万
依托单位：
University of Alaska Fairbanks Campus
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2018-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1700865&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建议：1700865/1703093目前可用的手动物料搬运安全评估工具基于静态提升条件，例如NIOSH提升方程，该方程给出了受试者静态提升的最大提升重量、距离和高度。另一种流行的方法来评估受伤的风险，手动材料处理是测量腰椎应力背痛。然而，这些方法都是静态的，不能准确地反映动态运动生成和伤害预防。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。本项目旨在开发一种有效的动态提升过程伤害风险评估和预测工具。项目结果将在人工物料搬运人机工程学中有潜在的广泛应用，并将进一步推进起重生物力学。该项目包括为本科生和研究生提供教育机会。将与阿拉斯加夏季研究学院合作，在阿拉斯加大学费尔班克斯为服务不足的少数民族初中和高中学生举办为期两周的夏季讲习班。此外，还将为当地从事起重工作的工人和德州理工大学的西班牙裔中学生举办为期一周的夏季研讨会。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，用于使用动态强度数据的肌肉骨骼模型进行动态手动材料搬运。动态强度数据将开发关键关节，如膝盖，臀部，脚踝，肘部和下脊柱。开发的用于预测动态提升系统中的伤害事件的工具将把当前的离线过程推进到在线、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：通过使用肌肉骨骼模型测量腰椎压缩和剪切应力，以及在关节间隙中，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）导出通用动态强度模型并从实验中验证模型参数; 2）引入腰椎肌肉模型并通过实验验证该模型;以及3）利用非线性规划算法来实现这些模型，以优化手动材料处理期间的动态提升运动，从而最小化损伤，并通过实验证明概念验证。将对关节内/关节间肌肉耦合进行建模，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中的动态强度参数将为模型提供改进的准确性和有效的计算，其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手段进行实验验证。该模型，它使近实时计算的动态手动材料处理作为时间的函数，可用于建立一个个人特定的动态限制提升过程，以及最佳的提升策略。项目结果将作为新的指导方针，人工材料处理人体工程学设计，考虑动态效应。此外，更好地理解在优化制定固有的人体生理系统中的各种约束，以及对这些约束的运动适应，将有助于人类运动研究领域，并补充现有的设计原则，手动材料处理。