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一种增强型全身模型,可改善上半身跟踪并减少复杂运动中的动态不一致性。

An Augmented Full-Body Model that Improves Upper Body Tracking and Reduces Dynamic Inconsistency in Complex Motion.

作者信息

Hu Xiao, Dooley Evan A, Stefanyshyn Darren J, Wannop John W, Russell Shawn D

机构信息

Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.

Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA.

出版信息

Ann Biomed Eng. 2025 Jun 3. doi: 10.1007/s10439-025-03762-7.

DOI:10.1007/s10439-025-03762-7
PMID:40461902
Abstract

PURPOSE

In recent years, the applications of musculoskeletal simulations have been expanded from simple walking to complex movements in various kinds of sports. The goal of this study was to augment the capability of the currently widely used full-body model (Rajagopal (2016) IEEE Trans. Biomed. Eng. 63:2068-2079) to improve the tracking of the kinematics of the head, shoulder, arms, and torso during complex full-body motion.

METHODS

Based on the testing of different modeling choices of neck, shoulder, and torso segments, the original Rajagopal full-body model was augmented by adding three joints in the spine and two sternoclavicular joints. The inverse kinematics and inverse dynamics of sports-related movements from 16 collegiate athletes were compared between the original Rajagopal and augmented full-body model.

RESULTS

Our results showed that the augmented full-body model had significant improvements in tracking errors of the markers on the head, arm, torso, and pelvis during inverse kinematics, which led to reduced dynamic inconsistency in inverse dynamics, compared to the Rajagopal model.

CONCLUSION

With a significant improvement in tracking the kinematics of the upper body, the augmented full-body model is a more suitable model to perform simulations involving complex full-body movements and is available for research use upon request from simtk.org.

摘要

目的

近年来,肌肉骨骼模拟的应用已从简单的行走扩展到各类运动中的复杂动作。本研究的目的是增强当前广泛使用的全身模型(拉贾戈帕尔(2016年),《IEEE生物医学工程汇刊》63:2068 - 2079)的能力,以改善复杂全身运动过程中头部、肩部、手臂和躯干运动学的跟踪。

方法

基于对颈部、肩部和躯干节段不同建模选择的测试,通过在脊柱中添加三个关节和两个胸锁关节对原始的拉贾戈帕尔全身模型进行了增强。比较了原始拉贾戈帕尔模型和增强全身模型之间16名大学生运动员与运动相关动作的逆运动学和逆动力学。

结果

我们的结果表明,与拉贾戈帕尔模型相比,增强全身模型在逆运动学过程中对头部、手臂、躯干和骨盆上标记物的跟踪误差有显著改善,这导致逆动力学中的动态不一致性降低。

结论

增强全身模型在上半身运动学跟踪方面有显著改进,是执行涉及复杂全身运动模拟的更合适模型,可应simtk.org的要求用于研究。

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