CN107736890B - 不同行走任务下膝关节内侧负载的估值方法 - Google Patents
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Abstract
本发明涉及医疗器械、生物力学测量,为确定不同不行任务下膝关节内侧负载与若干简单参数(如膝关节内收力矩、屈曲力矩和内收角度)之间的显著线性关系,用于膝关节内侧负载的简单估算。本发明,不同行走任务下膝关节内侧负载的估值方法,步骤如下:1)采集直行、踏步转弯和交叉转弯任务下下肢的运动学数据和地反作用力GRF;2)截取右腿支撑期作为分析的时间段;3)通过反向动力学可以由这段数据计算出右腿支撑期的膝关节内收力矩、屈曲力矩和内收角度;4)计算这段时间内的膝关节内侧负载;5)求出右腿支撑期膝关节内收力矩的最大值KAM以及出现时刻t;确定膝关节内侧负载估值公式。本发明主要应用于生物力学测量。
Description
技术领域
本发明涉及医疗器械、生物力学测量,具体讲,涉及一种针对不同行走任务的膝关节内侧负载估值方法。
背景技术
膝关节内侧负载是膝关节内侧骨关节炎发生和发展的重要影响因素。确定膝关节内侧负载最准确的方法是利用膝关节内部的传感器直接在体测量,但这种方法仅适用于进行膝关节置换的患者,不便于应用推广。为了弥补在体测量方法应用局限的缺点,研究人员通常使用肌骨模型估算肌肉的收缩力,进而无创计算膝关节内部的负载。
尽管肌骨模型可以较为准确地估算膝关节负载,但模型复杂,计算量大,耗时长。因此,一些研究学者尝试利用替代参数简单表征膝关节内测负载的变化。1991年,美国圣卢克斯医疗中心的Schipplein等人基于静定肌肉模型,首次提出膝关节内收力矩是膝关节内侧负载的主要决定参数。为了得到更准确的结果,美国佛罗里达大学的Zhao等人利用相关分析研究了膝关节内收力矩与在体测量的内侧负载之间的关系,确定了二者高度的相关性。虽然利用膝关节内收力矩代替膝关节内侧负载已经被大多数学者普遍接受,但仍有少数学者提出了不同的意见。美国佛罗里达大学的Walker等人研究了不同矫正步态下的膝关节内侧负载和内收力矩、屈曲力矩之间的关系,发现利用内收力矩峰值和屈曲力矩绝对值的峰值能够最为准确地估计内侧负载。日本学者Ogaya和德国学者Trepczynski等人也肯定了屈曲力矩在预测膝关节内侧负载中的作用。另外,加拿大学者Adouni和Shirazi-Adl认为膝关节负载的内外侧分布主要由膝关节内收角度决定。综上所述,膝关节内收力矩、屈曲力矩和内收角度都可以表征膝关节内侧负载的变化。
发明内容
为克服现有技术的不足,本发明旨在提出一种膝关节内侧负载估值方法。借助于反向动力学计算、肌骨模型和多元线性回归分析,确定不同不行任务下膝关节内侧负载与若干简单参数(如膝关节内收力矩、屈曲力矩和内收角度)之间的显著线性关系,该量化的线性关系可用于膝关节内侧负载的简单估算。该项发明简化了膝关节内侧负载的测量方法,为膝骨关节炎的临床治疗和康复评价提供了一种简便易行的方法。本发明采用的技术方案是,不同行走任务下膝关节内侧负载的估值方法,步骤如下:
1)利用运动捕捉***和三维测力板,采集直行、踏步转弯和交叉转弯任务下下肢的运动学数据和地反作用力GRF,运动学数据是指下肢关键位置处标记点的三维位置坐标;
2)截取右腿支撑期作为分析的时间段,即右脚踏在测力板上的时间,也就是GRF竖直分量大于1N的一段时间,这段时间内的运动学数据和GRF为要分析的原始数据;
3)通过反向动力学可以由这段数据计算出右腿支撑期的膝关节内收力矩、屈曲力矩和内收角度;
4)利用肌骨模型计算这段时间内的膝关节内侧负载;
5)求出右腿支撑期膝关节内收力矩的最大值KAM以及出现时刻t,确定t时刻的膝关节屈曲力矩KFM、内收角度KAA和内侧负载KMF;
6)将KMF作为结果变量,KAM,KAM和KFM,KAM、KFM和KAA依次作为预测变量,进行线性回归分析,确定膝关节内侧负载估值公式,如表1:
表1.不同行走任务下膝关节内侧负载估值公式
本发明的特点及有益效果是:
本技术通过反向动力学计算、肌骨模型和线性回归分析,确定了不同行走任务下膝关节内侧负载的估值模型。估值模型的建立可以有效简化膝关节内侧负载的确定,在临床步态分析中,能够快速准确估计不同步态下膝关节内侧负载的变化,实际操作性强,对临床膝关节内侧骨关节炎的康复治疗有重要的指导作用。
附图说明:
图1.膝关节内侧负载估值方法技术流程。
图2.不同直行任务示意图。
图3.标记点位置。
具体实施方式
不同行走任务(直行、踏步转弯和交叉转弯)下的膝关节内侧负载估值方法大体的技术流程如图1所示。
其具体步骤为:
1)利用运动捕捉***和三维测力板,采集直行、踏步转弯和交叉转弯任务下下肢的运动学数据和地反作用力GRF,运动学数据是指下肢关键位置处标记点的三维位置坐标(如图3);
2)截取右腿支撑期作为分析的时间段,即右脚踏在测力板上的时间,也就是GRF竖直分量大于1N的一段时间。这段时间内的运动学数据和GRF为要分析的原始数据。
3)通过反向动力学可以由这段数据计算出右腿支撑期的膝关节内收力矩、屈曲力矩和内收角度;
4)利用肌骨模型计算这段时间内的膝关节内侧负载;
5)求出右腿支撑期膝关节内收力矩的最大值KAM以及出现时刻,确定该时刻的膝关节屈曲力矩KFM、内收角度KAA和内侧负载KMF。
6)将KMF作为结果变量,KAM,KAM和KFM,KAM、KFM和KAA依次作为预测变量,进行线性回归分析,确定膝关节内侧负载估值公式(如表1)。由于交叉转弯中,KAA与KMF没有显著的相关性,因此在该任务下,不存在以KAM,KFM和KAA作为预测变量的估值模型。
表1.不同行走任务下膝关节内侧负载估值公式
本发明确定了不同行走任务下膝关节内侧负载的估值模型。本技术的最佳实施方案拟采用专利转让、技术合作或产品开发。
Claims (1)
1.一种不同行走任务下膝关节内侧负载的估值方法,其特征是,步骤如下:
1)利用运动捕捉***和三维测力板,采集直行、踏步转弯和交叉转弯任务下下肢的运动学数据和地反作用力GRF,运动学数据是指下肢关键位置处标记点的三维位置坐标;
2)截取右腿支撑期作为分析的时间段,即右脚踏在测力板上的时间,也就是GRF竖直分量大于1N的一段时间,这段时间内的运动学数据和GRF为要分析的原始数据;
3)通过反向动力学由所述这段时间内的运动学数据和GRF为要分析的原始数据计算出右腿支撑期的膝关节内收力矩、屈曲力矩和内收角度;
4)利用肌骨模型计算这段时间内的膝关节内侧负载;
5)求出右腿支撑期膝关节内收力矩的最大值KAM以及出现时刻t,确定t时刻的膝关节屈曲力矩KFM、内收角度KAA和内侧负载KMF;
6)将KMF作为结果变量,KAM,KAM和KFM,KAM、KFM和KAA依次作为预测变量,进行线性回归分析,确定膝关节内侧负载估值公式:
对于预测变量KAM,直行任务时,估值公式KMF=0.704+36.622×KAM,决定系数为0.736,踏步转弯任务时,估值公式KMF=0.560+41.054×KAM,决定系数为0.744,交叉转弯时,估值公式KMF=1.568+19.527×KAM,决定系数为0.171;
对于预测变量KAM、KFM,直行任务时,估值公式KMF=0.473+36.778×KAM+9.000×KFM,决定系数为0.795,踏步转弯任务时,估值公式KMF=0.448+38.206×KAM+8.467×KFM,决定系数为0.793,交叉转弯时,估值公式KMF=1.053+39.481×KAM-19.049×KFM,决定系数为0.519;
对于预测变量KAM、KFM、KAA,直行任务时,估值公式KMF=0.387+38.355×KAM+9.345×KFM+0.036×KAA,决定系数为0.809,踏步转弯任务时,估值公式KMF=0.371+38.178×KAM+10.098×KFM+0.037×KAA,决定系数为0.806。
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