L. Donisi, A. Coccia, F. Amitrano, Luca Mercogliano, G. Cesarelli, G. D'Addio
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Data were computed using a wearable inertial device for gait analysis: G-Walk System by BTS Bioengineering and analyzed through Inferential Statistics and Machine Learning. Overall, concerning Inferential Statistics carried out through ANOVA test for each motion parameter between free walk and walk with backpack, it was found that there is a significant statistical difference on 23 out of 30 motion parameters, of which 20 with maximum statistical significance (p<0.0001). Concerning Machine Learning analysis carried out through Random Forest algorithm considering free walk and walk with backpack as two different classes, it was found a high value of the overall Accuracy metric with a value of about 96%. Study results suggested that there is a drastic change in spatiotemporal and kinematic parameters related to gait underlining how the backpack alters the latter. 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引用次数: 12
摘要
虽然世界上有大量的学生使用不舒适和沉重的背包,但它们在时空和运动学参数方面对步态的负面影响仍未得到很好的研究。本文的目的是调查学校背包在执行Timed Up and Go测试期间的作用,试图确定它是否以及在多大程度上影响行走的时空和运动学参数,考虑到它是否可能与儿童腰痛相关。对98名10-12岁的儿童学生进行了以人群为基础的样本研究;记录性别、年龄、体重和下肢长度。数据采用BTS生物工程公司的可穿戴惯性步态分析装置G-Walk System进行计算,并通过推理统计和机器学习进行分析。总体而言,通过对自由行走与背背包行走各运动参数的方差分析进行推理统计,发现30个运动参数中有23个具有显著统计学差异,其中20个具有最大统计学意义(p<0.0001)。通过随机森林算法进行的机器学习分析,将自由行走和带背包行走作为两个不同的类别,发现整体精度度量值较高,约为96%。研究结果表明,与步态相关的时空和运动学参数发生了巨大变化,强调了背包如何改变后者。应正确考虑这些结果,以保护暴露于这些长期条件下的儿童的健康。
Backpack Influence on Kinematic Parameters related to Timed Up and Go (TUG) Test in School Children
Although a very large number of students in the world use uncomfortable and heavy backpacks, their negative influence on gait in terms of spatiotemporal and kinematic parameters are still not well investigated. The purpose of the paper is to investigate the role of the school backpack during the execution of the Timed Up and Go test trying to identify if and how much it affects walking in terms of spatiotemporal and kinematic parameters considering whether it might be correlated to low back pain in children. A population-based sample of 98 children students ages 10-12 years was studied; gender, age, weight and lower limb length were recorded. Data were computed using a wearable inertial device for gait analysis: G-Walk System by BTS Bioengineering and analyzed through Inferential Statistics and Machine Learning. Overall, concerning Inferential Statistics carried out through ANOVA test for each motion parameter between free walk and walk with backpack, it was found that there is a significant statistical difference on 23 out of 30 motion parameters, of which 20 with maximum statistical significance (p<0.0001). Concerning Machine Learning analysis carried out through Random Forest algorithm considering free walk and walk with backpack as two different classes, it was found a high value of the overall Accuracy metric with a value of about 96%. Study results suggested that there is a drastic change in spatiotemporal and kinematic parameters related to gait underlining how the backpack alters the latter. The results should be taken in correct account to safeguard children’s health exposed to these prolonged condition.