Autism Spectrum Disorders Gait Identification Using Ground Reaction Forces
Abstract: Autism spectrum
disorders (ASD) are a permanent neurodevelopmental disorder that can be identified
during the first few years of life and are currently associated with the
abnormal walking pattern. Earlier identification of this pervasive disorder
could provide assistance in diagnosis and establish rapid quantitative clinical
judgment. This paper presents an automated approach which can be applied to
identify ASD gait patterns using
three-dimensional (3D) ground reaction forces (GRF). The study involved classification
of gait patterns of children with ASD and typical healthy children. The GRF
data were obtained using two force plates during self-determined barefoot
walking. Time-series parameterizationtechniques were applied to the GRF
waveforms to extract the important gait features. The most dominant and correct
features for characterizing ASD gait were selected using statistical
between-group tests andstepwise discriminant analysis (SWDA). The selected
features were grouped into two groups which served as two input datasets to the
k-nearest neighbor (KNN) classifier. This study demonstrates that the 3D GRF gait
features selected using SWDA are reliable to be used in the identification of
ASD gait using KNN classifier with 83.33% performance accuracy.
Keywords: autism spectrum
disorders, gait classification, k-nearest neighbor, stepwise discriminant analysis,
ground reaction force
Author: Che Zawiyah Che Hasan,
Rozita Jailani, Nooritawati Md Tahir, Rohilah Sahak
Journal Code: jptkomputergg170066
