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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group Group-Personalized and Fully-Personalized Activity Classification Models
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Machine Learning to Quantify Physical Activity in Children with Cerebral Palsy: Comparison of Group Group-Personalized and Fully-Personalized Activity Classification Models

机译：机器学习以脑瘫儿童量化体育活动：集团集团个性化和全部个性化活动分类模型的比较

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Pattern recognition methodologies, such as those utilizing machine learning (ML) approaches, have the potential to improve the accuracy and versatility of accelerometer-based assessments of physical activity (PA). Children with cerebral palsy (CP) exhibit significant heterogeneity in relation to impairment and activity limitations; however, studies conducted to date have implemented “one-size fits all” group (G) models. Group-personalized (GP) models specific to the Gross Motor Function Classification (GMFCS) level and fully-personalized (FP) models trained on individual data may provide more accurate assessments of PA; however, these approaches have not been investigated in children with CP. In this study, 38 children classified at GMFCS I to III completed laboratory trials and a simulated free-living protocol while wearing an ActiGraph GT3X+ on the wrist, hip, and ankle. Activities were classified as sedentary, standing utilitarian movements, or walking. In the cross-validation, FP random forest classifiers (99.0–99.3%) exhibited a significantly higher accuracy than G (80.9–94.7%) and GP classifiers (78.7–94.1%), with the largest differential observed in children at GMFCS III. When evaluated under free-living conditions, all model types exhibited significant declines in accuracy, with FP models outperforming G and GP models in GMFCS levels I and II, but not III. Future studies should evaluate the comparative accuracy of personalized models trained on free-living accelerometer data.
机译：模式识别方法，例如利用机器学习（ML）方法，有可能提高基于加速度计的身体活动评估（PA）的准确性和多功能性。患有脑瘫（CP）的儿童与损伤和活动限制有关的显着异质性;然而，迄今为止进行的研究已经实施了“单尺寸适合所有”组（G）模型。针对大型电机函数分类（GMFC）级别的组特性（GP）模型和在各个数据上培训的全部个性化（FP）模型可能提供更准确的PA评估;但是，这些方法尚未在CP的儿童中进行调查。在本研究中，38名儿童在GMFCS I到III完成了实验室试验和模拟的自由生活方案，同时在手腕，臀部和脚踝上佩戴了Actigraph Gt3x +。活动被归类为久坐，站立的功利运动，或走路。在交叉验证中，FP随机森林分类器（99.0-99.3％）表现出比G（80.9-94.7％）和GP分类器（78.7-94.1％）的高精度（78.7-94.1％），在GMFCS III的儿童中观察到最大的差异。在自由生活条件下进行评估时，所有模型类型的准确性都表现出显着下降，FP模型在GMFCS级别I和II中表现出G和GP模型，但不是III。未来的研究应评估在自由生活加速度计数据上培训的个性化模型的比较准确性。

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