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使用Markerless驱动的肌肉骨模型预测步行动力学

Zachary Ripic¹, Ilias Theodorakos², Michael S Andersen²

¹Department of Kinesiology and Sport Sciences, University of Miami, Miami, FL, United States; Sports Medicine Institute, University of Miami Miller School of Medicine, Miami, FL, United States.

Journal of biomechanics

|July 8, 2023

概括

这项研究使用无标记运动捕捉和肌肉骨建模来预测行走过程中的地面反应力和时刻. 虽然力预测有希望,但瞬间预测需要进一步精细化,以便在临床上使用.

科学领域:

生物力学生物力学
运动分析运动分析
肌肉骨系统建模

背景情况:

基于视频的运动分析正在生物力学中取得进展.
关于从无标记动力学和肌肉骨模型中预测动力学的研究有限.
准确的动力预测对于理解人类运动至关重要.

研究的目的:

预测地面反应力 (GRF) 和地面反应时刻 (GRM) 在地面行走过程中.
将RGB无标记动力学集成到肌肉骨建模框架中,用于动力预测.
将无标记驱动的GRF/GRM预测与力板测量进行比较.

主要方法:

使用全身无标记器的动力学数据作为输入.
采用肌肉骨模型来得出GRF和GRM预测.
与力板数据对比的验证预测.

主要成果:

无标记驱动的GRF预测显示了与测量值的中等到很好的一致性 (RMSE:ML=0.035,AP=0.070,V=0.155N·BW-1).
电路电路的相关性和ICCs表明中等到良好的协议.
在GRM预测中,与横向平面,正面平面和横向平面 (RMSE在0.005到0.029Nm·BWH-1之间) 的测量值的一致性较差.

结论:

没有RGB标记的动力学与肌肉骨建模相结合,可以预测步行过程中的GRF,并且具有有希望的准确性.
目前,GRM预测的准确性不足以广泛研究或临床应用.
方法论上的考虑需要解决,以改进这种方法的未来代.

关键词:

人工智能的人工智能是人工智能.生物力学生物力学预测地面反应力的预测.无标记的运动捕捉.肌肉骨系统的建模

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