Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models

Abstract

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.