A neural network model to predict knee adduction moment during walking based on ground reaction force and anthropometric measurements.
A neural network model to predict knee adduction moment during walking based on ground reaction force and anthropometric measurements.
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DOI：
10.1016/j.jbiomech.2011.11.057
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发表时间：
2012-02-23
影响因子：
2.4
通讯作者：
Andriacchi TP
中科院分区：
文献类型：
--
作者：
Favre J;Hayoz M;Erhart-Hledik JC;Andriacchi TP
外部膝关节内收矩（KAM）是评估步行过程中膝盖荷载的主要变量，特别是在膝关节骨关节炎的患者中。但是，对KAM的评估仅限于提供全运动实验室的位置。这项研究的目的是开发和测试一种仅使用力板和人体测量测量值来预测KAM的简单方法。研究了三组28个膝盖（无症状，轻度骨关节炎和严重的骨关节炎）。使用运动捕获系统和力板以不同的速度收集步行试验。参考KAM通过反动力学计算。为了进行预测，使用来自地面反应力和机械轴比对的11个输入设计了受试者的人工神经网络。预测的KAM曲线类似于中位平均绝对偏差（MAD）为0.36％BW*HT的参考曲线，在756个单个试验中，中位相关系数为0.966。当比较平均组曲线时，MAD中位数为0.09％BW*HT，中位相关系数为0.998。从预测曲线和参考曲线中提取的峰值和角冲动显着相关，当预测或参考曲线用于比较的95％时，这三组之间在三组之间获得了显着差异。总之，这项研究表明，使用通用人工神经网络的简单方法可以预测具有高显着性的行走过程中的KAM曲线，并为更广泛评估KAM提供了一种实用的选择。
The external knee adduction moment (KAM) is a major variable for the evaluation of knee loading during walking, specifically in patients with knee osteoarthritis. However, assessment of the KAM is limited to locations where full motion laboratories are available. The purpose of this study was to develop and test a simple method to predict the KAM using only force plate and anthropometric measurements. Three groups of 28 knees (asymptomatic, mild osteoarthritis, and severe osteoarthritis) were studied. Walking trials were collected at different speeds using a motion capture system and a force plate. The reference KAM was calculated by inverse dynamics. For the prediction, inter subject artificial neural networks were designed using 11 inputs coming from the ground reaction force and the mechanical axis alignment. The predicted KAM curves were similar to the reference curves with median mean absolute deviation (MAD) of 0.36%BW*Ht and median correlation coefficient of 0.966 over 756 individual trials. When comparing mean group curves, the median MAD was 0.09%BW*Ht and the median correlation coefficient 0.998. The peak values and the angular impulses extracted from the predicted and reference curves were significantly correlated, and the same significant differences were obtained among the three groups when the predicted or when the reference curves were used for 95% of the comparisons. In conclusion, this study demonstrated that a simple method using a generic artificial neural network can predict the KAM curve during walking with a high level of significance and provides a practical option for a broader evaluation of the KAM.
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