Collaborative Research: Joint Space Muscle Fatigue Model and Integration into Full Body Motion Prediction for Repetitive Dynamic Tasks

合作研究：关节空间肌肉疲劳模型并集成到重复动态任务的全身运动预测中

• 批准号: 2014278
• 负责人: James Yang
• 金额: $ 30.79万
• 依托单位: Texas Tech University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014278&HistoricalAwards=false
• 关键词: Collaborative; Research; Joint; Space; Muscle

Worker fatigue increases the risk for illnesses and injuries. An estimated annual cost in the US of over $130 billion is from fatigue-related lost productive work time to employers, which indicates that fatigue needs to be considered during the workplace safety design process. Although different muscle fatigue models have been developed, all were applied to isometric contractions (contractions without muscle shortening), but the majority of everyday activities are involved in concentric (muscle contracts/shortens) and eccentric (muscle lengthens/returns to resting state) muscle movements, i.e., repetitive dynamic tasks. Conventional motion simulation approaches for injury prevention typically optimize a motion without considering muscle fatigue. Thus, the goal of this project is to address the need for a musculoskeletal model that can predict muscle movement considering muscle fatigue. The model can be adapted to the physical properties of an individual worker, e.g., height, weight, length of body segments, etc. The methods and associated numerical tools developed will be applicable to broad occupational health and safety designs such as lower back injury prevention for repetitive lifting and repetitive package handling in the delivery industry. The project will also enable education and training for undergraduates, graduate students, and store employees. Results will be integrated into courses for Biomechanics and Digital Human Modeling and made available for future generations of engineers. In addition, a week-long summer camp will be organized for local store managers with lifting jobs at Texas Tech University. For these managers this summer camp will help them to understand the injury mechanism and causes for injuries, contributing to their awareness of work-related injuries whenever employees conduct repetitive lifting tasks daily.The goal of this project is to develop a novel and efficient dynamic motion prediction tool considering muscle fatigue for repetitive dynamic tasks, which, if successful, will be the first full body biomechanics human model with this capability. The project’s objectives are to: develop: a new joint space muscle fatigue model for repetitive dynamic tasks; develop a new joint space predictive simulation method considering fatigue; and decompose the fatigued joint torques into fatigued muscle forces. The Research Plan is organized under 6 tasks. TASK 1 is to develop a three-compartment joint space fatigue model for repetitive tasks beginning with a 3D musculoskeletal model that has 30 DOFS, 21 segments, 324 musculotendon actuators and 5 lumbar vertebrae connected with 6 DOF joints. Fatigue is incorporated by bundling all muscles responsible for each joint into one virtual muscle with virtual units being divided into compartments depending on the state in which they are in: active, fatigued or resting. TASK 2 is to perform an inverse dynamics-based optimization without fatigue. The design variables for the skeleton optimization problem are joint angles from which joint torques can be computed. TASK 3 is to optimize join space motion prediction considering fatigue using collocation methods. The joint torques obtained under Task 2 will be used to calculate a “target load” vector that will be used to initiate the fatigue process. The optimization problem is to find the optimal joint angles, joint torques, joint resultant and virtual muscle active states that minimize the cost function of normalized joint torque squared subject to model dynamics equations of motion. TASK 4 is to find the lower extremity and lumbar spine model muscle forces corresponding to the fatigued joint torques using static optimization. Tasks 1-4 are interconnected elements of a complete nonlinear motion optimization considering muscle fatigue for dynamic tasks. TASK 5 is to collect experiment related data from 20 subjects (10 males and 10 females) of varying ages, statures, and BMIs. The data will be used to validate the joint space muscle fatigue model and skeletal motion prediction. TASK 6 is to validate the muscle fatigue model for repetitive dynamic tasks involving wrist, elbow, shoulder, trunk hip, knee and ankle joints and then to validate the 3D motion prediction model considering muscle fatigue during a repetitive box lifting process. For joint related validations, 8 subjects of each gender will be used to tune the model and the 2 remaining subjects will be used for validation. For 3D motion prediction validation, three aspects (muscle levels, joint profiles and ground reaction forces), model predictions will be compared to aspects determined from EMG and motion capture data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

工人疲劳增加了患病和受伤的风险。 据估计，美国每年因疲劳而损失的生产性工作时间给雇主造成的损失超过1300亿美元，这表明在工作场所安全设计过程中需要考虑疲劳问题。 虽然已经开发了不同的肌肉疲劳模型，但所有模型都应用于等长收缩（没有肌肉缩短的收缩），但大多数日常活动都涉及同心（肌肉收缩/缩短）和偏心（肌肉延长/返回到静止状态）肌肉运动，即，重复的动态任务。用于损伤预防的常规运动模拟方法通常在不考虑肌肉疲劳的情况下优化运动。 因此，该项目的目标是解决对肌肉骨骼模型的需求，该模型可以预测考虑肌肉疲劳的肌肉运动。 该模型可以适应单个工人的物理特性，例如，身高，体重，身体部分的长度等开发的方法和相关的数值工具将适用于广泛的职业健康和安全设计，如在交付行业的重复提升和重复包装处理的下背部损伤预防。该项目还将为本科生、研究生和商店员工提供教育和培训。 研究结果将被整合到生物力学和数字人体建模课程中，供未来几代工程师使用。此外，还将为当地商店经理组织为期一周的夏令营，他们将在德克萨斯理工大学从事举重工作。本项目的目的是开发一种新颖、高效的动态运动预测工具，在考虑肌肉疲劳的情况下预测重复性动态任务的运动，如果能够成功，将是第一个具有这种能力的全身生物力学人体模型。 该项目的目标是：开发：一个新的关节空间肌肉疲劳模型，用于重复性动态任务;开发一个新的关节空间预测模拟方法，考虑疲劳;并将疲劳的关节扭矩分解为疲劳的肌肉力量。 研究计划分为6个任务。 任务1是开发用于重复性任务的三室关节空间疲劳模型，从3D肌肉骨骼模型开始，该模型具有30 DOFS，21个节段，324个肌肉肌腱致动器和5个腰椎与6个DOF关节连接。 疲劳是通过将负责每个关节的所有肌肉捆绑到一个虚拟肌肉中来实现的，其中虚拟肌肉单元根据它们所处的状态被划分为隔间：活动，疲劳或休息。 任务2是在没有疲劳的情况下执行基于逆动力学的优化。 骨架优化问题的设计变量是关节角度，由此可以计算关节扭矩。 任务3是使用配置方法优化考虑疲劳的连接空间运动预测。 任务2中获得的关节扭矩将用于计算“目标载荷”矢量，该矢量将用于启动疲劳过程。 优化问题是找到最佳的关节角度，关节扭矩，关节合力和虚拟肌肉活动状态，使标准化关节扭矩平方的成本函数最小化模型动力学运动方程。 任务4是使用静态优化找到与疲劳关节扭矩相对应的下肢和腰椎模型肌肉力。 任务1-4是考虑动态任务肌肉疲劳的完整非线性运动优化的相互关联的元素。 任务5是从不同年龄、身高和BMI的20名受试者（10名男性和10名女性）收集实验相关数据。 这些数据将用于验证关节空间肌肉疲劳模型和骨骼运动预测。 任务6是验证涉及腕关节、肘关节、肩关节、躯干髋关节、膝关节和踝关节的重复性动态任务的肌肉疲劳模型，然后验证在重复性提箱过程中考虑肌肉疲劳的3D运动预测模型。 对于关节相关确认，将使用每种性别的8名受试者调整模型，剩余2名受试者将用于确认。 对于3D运动预测验证，三个方面（肌肉水平，关节轮廓和地面反作用力），模型预测将与EMG和运动捕捉数据确定的方面进行比较。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Sensitivity analysis of sex- and functional muscle group-specific parameters for a three-compartment-controller model of muscle fatigue

肌肉疲劳三室控制器模型的性别和功能性肌群特异性参数的敏感性分析

• DOI: 10.1016/j.jbiomech.2022.111224
• 发表时间: 2022
• 期刊: Journal of Biomechanics
• 影响因子: 2.4
• 作者: Rakshit, Ritwik;Barman, Shuvrodeb;Xiang, Yujiang;Yang, James
• 通讯作者: Yang, James

Assessments and Evaluation Methods for Upper Limb Exoskeleton - a Literature Survey

上肢外骨骼的评估和评价方法——文献调查

• DOI: 10.1115/detc2022-88968
• 发表时间: 2022
• 期刊: ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
• 作者: Lee, Seunghun;Xiang, Yujiang;Xia, Ting;Yang, James
• 通讯作者: Yang, James

Optimization-based biomechanical lifting models for manual material handling: A comprehensive review

基于优化的手动物料搬运生物力学提升模型：全面综述

• DOI: 10.1177/09544119221114208
• 发表时间: 2022
• 期刊: Part H: Journal of Engineering in Medicine
• 作者: Zaman, Rahid;Arefeen, Asif;Quarnstrom, Joel;Barman, Shuvrodeb;Yang, James;Xiang, Yujiang
• 通讯作者: Xiang, Yujiang

Functional muscle group- and sex-specific parameters for a three-compartment controller muscle fatigue model applied to isometric contractions

应用于等长收缩的三室控制器肌肉疲劳模型的功能性肌群和性别特定参数

• DOI: 10.1016/j.jbiomech.2021.110695
• 发表时间: 2021
• 期刊: Journal of Biomechanics
• 影响因子: 2.4
• 作者: Rakshit, Ritwik;Xiang, Yujiang;Yang, James
• 通讯作者: Yang, James

Joint fatigue-based optimal posture prediction for maximizing endurance time in box carrying task

• DOI: 10.1007/s11044-022-09832-1
• 发表时间: 2022-06-20
• 期刊: MULTIBODY SYSTEM DYNAMICS
• 影响因子: 3.4
• 作者: Barman, Shuvrodeb;Xiang, Yujiang;Yang, James
• 通讯作者: Yang, James