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An application of principal component analysis for lower body kinematics between loaded and unloaded walking.

机译:主成分分析在有载行走和无载行走之间的下半身运动学中的应用。

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摘要

Load carriage is a very common daily activity at home and in the workplace. Generally, the load is in the form of an external load carried by an individual, it could also be the excessive body mass carried by an overweight individual. To quantify the effects of carrying extra weight, whether in the form of an external load or excess body mass, motion capture data were generated for a diverse subject set. This consisted of twenty-three subjects generating one hundred fifteen trials for each loading condition. This study applied principal component analysis (PCA) to motion capture data in order to analyze the lower body gait patterns for four loading conditions: normal weight unloaded, normal weight loaded, overweight unloaded and overweight loaded. PCA has been shown to be a powerful tool for analyzing complex gait data. In this analysis, it is shown that in order to quantify the effects of external loads and/or for both normal weight and overweight subjects, the first principal component (PC1) is needed. For the work in this paper, PCs were generated from lower body joint angle data. The PC1 of the hip angle and PC1 of the ankle angle are shown to be an indicator of external load and BMI effects on temporal gait data.
机译:装载运输是在家中和工作场所中非常普遍的日常活动。通常,负载是由个人承担的外部负载的形式,也可能是超重的个人承担的过多体重。为了量化承受额外重量的影响,无论是外部负载还是过多的体重,都针对不同的受试者组生成了运动捕捉数据。它由23位受试者组成,每种载荷条件产生115次试验。这项研究将主成分分析(PCA)应用于运动捕获数据,以分析四种负荷情况下的下半身步态模式:正常体重未负荷,正常体重已负荷,超重未负荷和超重负荷。 PCA已被证明是用于分析复杂步态数据的强大工具。在此分析中显示,为了量化外部负荷的影响和/或对于正常体重和超重受试者,都需要第一主成分(PC1)。对于本文的工作,PC是从下半身关节角度数据生成的。髋部角度的PC1和踝部角度的PC1显示为外部负荷和BMI对时间步态数据的影响的指标。

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