Subject-specific strength percentile determination for two-dimensional symmetric lifting considering dynamic joint strength

Subject-specific strength percentile determination for two-dimensional symmetric lifting considering dynamic joint strength
复制标题

考虑动态关节强度的二维对称提升的特定主题强度百分位确定

DOI:
10.1007/s11044-018-09661-1
复制
发表时间:
2019
影响因子:
3.4
通讯作者:
Yang, James
Yang, James
中科院分区:
工程技术2区
文献类型:
--
作者:
Xiang, Yujiang;Zaman, Rahid;Rakshit, Ritwik;Yang, James

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本文介绍了一种有效的优化方法,确定特定的主题强度百分位数和预测的最大举重运动,考虑动态关节力量在对称箱提升。动态强度被建模为关节角度和关节角速度的三维函数的基础上,从文献中实验获得的关节强度数据。该函数进一步制定为关节扭矩限制约束的逆动力学优化制定预测最大举重运动。从实验中获得的初始,中期和最终的姿态,并施加在优化配方的跟踪约束。此外,箱重量和持续时间作为输入的起重优化问题。归一化关节扭矩平方被用作目标函数。枚举受试者特异性强度百分位数(),直至获得最佳解决方案。所确定的强度百分位数是考虑二维对称提升任务的所有关节的相互作用的全局分数。结果表明,在极端的升力条件下,结合动强度是预测升力运动的关键。所提出的算法可以确定特定主题的强度百分位数的基础上,实验箱提升数据。准确的力量百分位数对于预测与力量相关的任务以保护工人免受伤害至关重要。
This paper describes an efficient optimization method for determining the subject-specific strength percentile and predicting the maximum weight lifting motion by considering dynamic joint strength in symmetric box lifting. Dynamic strength is modeled as a three-dimensional function of joint angle and joint angular velocity based on experimentally obtained joint strength data from the literature. The function is further formulated as the joint torque limit constraint in an inverse dynamics optimization formulation to predict the maximum weight lifting motion. The initial, mid-time, and final postures are obtained from experiments and imposed as tracking constraints in the optimization formulation. In addition, the box weight and time duration are given as inputs for the lifting optimization problem. The normalized joint torque squared is used as the objective function. Subject-specific strength percentile () is enumerated until the optimal solution is achieved. The determined strength percentile is a global score considering interactions of all joints for the two-dimensional symmetric lifting task. Results show that incorporating dynamic strength is critical in predicting lifting motion in extreme lifting conditions. The proposed algorithm can determine the subject-specific strength percentile based on experimental box lifting data. The accurate strength percentile is critical to predict strength related tasks to protect workers from injury.
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