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Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

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

Katharine Goldthorp¹, Benn Henderson², Pratheepan Yogarajah²

¹School of Psychology and Sports Science, Bangor University, Bangor LL57 2DG, UK.

|July 29, 2025

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This summary is machine-generated.

Autism spectrum disorder (ASD) is linked to atypical gait. Temporal gait analysis and machine learning can differentiate individuals with ASD from typically developing peers, suggesting potential as a diagnostic aid.

Area of Science:

Neuroscience
Biomechanical Engineering
Developmental Psychology

Background:

Motor deficits, particularly atypical gait, are frequently observed in individuals with autism spectrum disorder (ASD).
The underlying mechanisms and precise characteristics of gait differences in ASD remain incompletely understood.
Gait timing presents a measurable and accessible metric for exploring motor differences.

Purpose of the Study:

To investigate if temporal gait parameters alone can characterize autistic gait.
To determine if temporal gait analysis, enhanced by machine learning, can serve as a classifier between individuals with ASD and typically developing (TD) individuals.
To explore the potential of gait timing analysis as a diagnostic tool for ASD.

Main Methods:

High-resolution temporal analysis of gait was conducted on two groups of male participants: high-functioning ASD (N=16) and TD (N=16), aged 7–35 years.

Keywords:

ASD gait machine learning timing variability

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Data were collected using a VICON® 3D motion analysis system.

Machine learning models, including random forest, were applied to temporal gait variability data for classification.

Main Results:

The ASD group exhibited significantly increased temporal variability across all tested gait parameters compared to the TD group (p < 0.001).
Machine learning analysis demonstrated that temporal gait variability effectively classified participants into ASD and TD groups.
Random forest emerged as the best-performing model among twelve tested algorithms.

Conclusions:

Temporal gait analysis reveals significant differences in gait timing variability between individuals with ASD and TD individuals.
Machine learning algorithms can effectively utilize gait timing variability for group classification.
This approach holds promise as a potential future diagnostic aid for ASD.