Increased Temporal Variability of Gait in ASD: A Motion Capture and Machine Learning Analysis

Research suggests that autistic peoples’ walking style may be subtly different to that of typically developing people. These differences have been shown by using advanced movement analysis called gait analysis; they cannot be seen by just watching someone walking. Previous studies, however, have produced conflicting results, perhaps because of their diverse methods and often complex approaches. We set out to test if two groups of people, one group with autism and the other a typically developing group, could be distinguished simply in terms of the micro-timing of their walking rhythm and, if an artificial intelligence technique, called machine learning, could be trained to make this classification. We found that the autistic group’s walking rhythm was clearly more variable, but on average not faster or slower, and that machine learning algorithms, trained on gait timing alone, could be used for group classification. Further validation of gait timing variability in autism is encouraged, possibly leading to a semi-automated test to assist clinicians in the early stages of their assessments, and to a fuller understanding of the nature of autism. Tests that facilitate diagnosis could lead to families being offered help sooner.