Anomaly Gait Detection in ASD Children based on Markerless-based Gait Features

Abstract

Children with autism are known for their difficulties in social interaction, communication, and behaviour characteristics. Hence, this study proposed to develop a markerless-based gait method for anomaly gait detection in children with autism spectrum disorder (ASD). Firstly, a depth sensor is used during walking gait data collection of the 23 ASD children and 30 typical healthy developing (TD) children. Further, these walking gait data are divided into the Reference Joint (REF) and Direct Joint (DIR) features. For each type, five sets of features are derived that represents the whole body, upper body, lower body, the right and left side of the body. The three classifiers used to validate the effectiveness of the proposed method are Naïve Bayes (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Results showed that the highest accuracy, precisely 94.22%, is achieved using the ANN classifier with DIR1 gait features representing the whole body. The highest sensitivity and specificity obtained are 94.49% and 93.93% accordingly. In addition, the proposed markerless model using the DIR1 gait features and the ANN as classifier also outperformed previous studies that have utilised the marker-based model. This promising result showed that the proposed method could be used for early intervention for the ASD group. The markerless-based gait technique also has fewer experiment protocols, thus causing the ASD children to feel more comfortable.