步态分析中受试者内部差异对机器学习模型中ASD分类性能的影响

Benn Henderson, P. Yogarajah, B. Gardiner, M. McGinnity, Kitty Forster, B. Nicholas, D. Wimpory, J. Wanigasinghe
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摘要

自闭症谱系障碍(ASD)是一种全球普遍存在的发育障碍。传统上,检测自闭症的研究主要集中在症状的行为方面,然而,最近,焦点已经转移到使用机器学习和步态分析等技术的更客观的替代方案上。步态测量,已经被用于人的识别,因人而异,引入了很多受试者内部方差。这适用于本研究中使用的8个时空特征,代表个体在步态周期的每个阶段花费的时间,使用Vicon运动跟踪系统收集。这些特征在受试者进行的每次步态试验中平均,产生第二组特征,受试者内部方差减少。四种常用分类器,支持向量机(SVM), k近邻(KNN),随机森林(RF)和决策树(DT)分类器,都使用两个特征集进行训练,并比较它们的分类率。结果表明,对于射频分类器来说,通过减小主题内方差,可以成功地提高分类能力。KNN和DT分类器的准确性下降最小,其中SVM在受试者内方差降低时损失最大。结果表明,主体内方差对分类能力的影响在很大程度上取决于分类器对初始问题的适用性以及数据的大小和类别平衡。
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Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models
Autism Spectrum Disorder (ASD) is a developmental disorder that is prevalent globally. Research into detecting autism traditionally focused on behavioural aspects of the condition, however, more recently, focus has shifted to more objective alternatives using techniques such as machine learning and gait analysis. Gait measurements, having been used for person identification, varies from person to person, introducing a lot of intra-subject variance. This applies to the 8 spatial-temporal features used in this study, representing the time that an individual spends in each phase of a gait cycle, collected using a Vicon motion tracking system. The features were averaged across each gait trial that the subjects performed, producing a second set of features with reduced intra-subject variance. Four common classifiers, a Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forests (RF) and a Decision Tree (DT) classifier, were all trained using the two feature sets and their classification rates were compared. The results show that for the RF classifier, reducing the intra-subject variance, was able to successfully increase the classification power. The KNN and DT classifiers experienced a minimal decrease in accuracy, where the SVM suffered the greatest loss when intra-subject variance was reduced. Results overall show that the effect intra-subject variance has on classification power depends heavily on the suitability of the classifier to the initial problem as well as size and class balance of the data.
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