Automated Classification of Autism Spectrum Disorders Gait Patterns Using Discriminant Analysis Based on Kinematic and Kinetic Gait Features

Autism spectrum disorder (ASD) is a permanent neurodevelopmental disorder that can be recognised during the first few years of life and is further supported by the existence of gait impairments. Automated classification of ASD gait could provide assistance in diagnosis and ensure rapid quantitative clinical judgement. This study proposes an automated classification of ASD gait patterns based on kinematic and kinetic gait features with the application of machine learning approaches. Gait analysis of 24 ASD and 24 typical healthy children were recorded using a state-of-the-art three-dimensional (3D) motion analysis system and two force platforms during barefoot self-selected normal walking. Nine kinematic and sixteen kinetic gait features were statistically selected using the independent t-tests and Mann-Whitney U tests, which grouped into two types of datasets. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were employed to perform the recognition task. Overall, the results of the proposed study suggest that LDA classifier with kinetic gait features as input predictors produces better classification performance with 82.50% of accuracy and lower misclassification rate.