Autism Spectrum Disorder and Normal Gait Classification Using Machine Learning Approach

Che Zawiyah Che Hasan, Rozita Jailani, Nooritawati Md Tahir


Previous research has reported that children with autism spectrum disorder (ASD) exhibit unusual movement and atypical gait patterns. Automated classification of abnormal gait from normal gait can serve as a potential tool for early and objective diagnosis as well as post-treatment monitoring. The aim of this study is to employ machine learning approaches to differentiate between children with ASD and healthy controls by utilizing gait features extracted from three-dimensional (3D) gait analysis data. The gait data of 30 children with ASD and 30 healthy controls were obtained using 3D gait analysis during walking at a normal pace. Time-series parameterization techniques were applied to the kinematic and kinetic waveforms to extract useful gait features. Further, the dominant gait features were selected using statistical feature selection techniques. To highlight the efficacy of different machine learning classifiers towards devising an accurate gait classification, four machine learning classifiers were trained to classify ASD and control gait based on the selected dominant gait features. The classifiers are Artificial Neural Networks (ANN), Support Vector Machines (SVMs), K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA). The 10-fold cross-validation test results indicate that the ANN-SCG classifier with six dominant gait features was able to produce the optimum classification performance with 98.3% accuracy, 96.7% sensitivity, and 100% specificity. The findings indicate that the ANN classifier has the potential to serve as a valuable tool for assisting in the diagnosis of ASD gait and evaluating treatment programs.


Machine Learning; Gait Classification; Autism Spectrum Disorder; Three-Dimensional Gait Analysis; Gait Pattern

Full Text:



American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Arlington, VA: American Psychiatric Association, 2013.

S. Rahman, S. F. Ahmed, O. Shahid, M. A. Arrafi, and M. A. R. Ahad, “Automated Detection Approaches to Autism Spectrum Disorder Based on Human Activity Analysis: A Review,” Cognit. Comput., vol. 14, no. 5, pp. 1773–1800, 2022.

B. Zablotsky, L. I. Black, M. J. Maenner, L. A. Schieve, and S. J. Blumberg, “Estimated prevalence of autism and other developmental disabilities following questionnaire changes in the 2014 National Health Interview Survey,” 2015.

Y. Li, M. A. Mache, and T. A. Todd, “Automated identification of postural control for children with autism spectrum disorder using a machine learning approach,” J. Biomech., vol. 113, pp. 1–7, 2020.

A. Pradhan, V. Chester, and K. Padhiar, “Classification of Autism and Control Gait in Children Using Multisegment Foot Kinematic Features,” Bioengineering, vol. 9, no. 10, pp. 1–15, 2022.

L. Gong, Y. Liu, L. Yi, J. Fang, Y. Yang, and K. Wei, “Abnormal Gait Patterns in Autism Spectrum Disorder and Their Correlations with Social Impairments,” Autism Res., vol. 13, no. 7, pp. 1215–1226, 2020.

C. Z. C. Hasan, R. Jailani, N. M. Tahir, and S. Ilias, “The analysis of three-dimensional ground reaction forces during gait in children with autism spectrum disorders,” Res. Dev. Disabil., vol. 66, pp. 55–63, 2017.

C. Z. C. Hasan, R. Jailani, N. M. Tahir, and H. M. Desa, “Vertical ground reaction force gait patterns during walking in children with autism spectrum disorders,” Int. J. Eng. Trans. B Appl., vol. 31, no. 5, pp. 705–711, 2018.

J. S. Dufek, J. D. Eggleston, J. R. Harry, and R. A. Hickman, “A comparative evaluation of gait between children with autism and typically developing matched controls,” Med. Sci., vol. 5, no. 1, pp. 1–11, 2017.

J. D. Eggleston, J. R. Harry, R. A. Hickman, and J. S. Dufek, “Analysis of gait symmetry during over-ground walking in children with autism spectrum disorder,” Gait Posture, vol. 55, pp. 162–166, 2017.

J. D. Eggleston, M. R. Landers, B. T. Bates, E. Nagelhout, and J. S. Dufek, “Weighted Walking Influences Lower Extremity Coordination in Children on the Autism Spectrum,” Percept. Mot. Skills, vol. 125, no. 6, pp. 1103–1122, 2018.

E. B. Torres and A. M. Donnellan, Autism: The movement perspective. Frontiers Media SA, 2015.

S. Zahan, Z. Gilani, G. M. Hassan, and A. Mian, “Human Gesture and Gait Analysis for Autism Detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 3327–3336.

G. Esposito and P. Venuti, “Gait analysis in children with autism spectrum disorder and in children with typical development,” Gait Posture, vol. 24, pp. 270–271, 2006.

M. Calhoun, M. Longworth, and V. L. Chester, “Gait patterns in children with autism,” Clin. Biomech., vol. 26, no. 2, pp. 200–206, 2011.

M. J. Weiss, M. F. Moran, M. E. Parker, and J. T. Foley, “Gait analysis of teenagers and young adults diagnosed with autism and severe verbal communication disorders,” Front. Integr. Neurosci., vol. 7, no. 33, pp. 1–10, 2013.

R. K. Begg, M. Palaniswami, and B. Owen, “Support vector machines for automated gait classification,” IEEE Trans. Biomed. Eng., vol. 52, no. 5, pp. 828–838, 2005.

J. L. McGinley, R. Baker, R. Wolfe, and M. E. Morris, “The reliability of three-dimensional kinematic gait measurements: A systematic review,” Gait Posture, vol. 29, no. 3, pp. 360–369, 2009.

D. T. H. Lai, R. K. Begg, and M. Palaniswami, “Computational intelligence in gait research: a perspective on current applications and future challenges,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 5, pp. 687–702, 2009.

R. Baker, Measuring Walking: A Handbook of Clinical Gait Analysis, no. 1. London, United Kingdom: Mac Keith Press, 2013.

C. Pradhan et al., “Automated classification of neurological disorders of gait using spatio-temporal gait parameters,” J. Electromyogr. Kinesiol., vol. 25, no. 2, pp. 413–422, 2015.

M. Mostafavizadeh, A. R. Sadri, and M. Zekri, “Walking pattern classification in children with cerebral palsy: A wavelet network approach,” 16th CSI Int. Symp. Artif. Intell. Signal Process. (AISP 2012), pp. 243–249, 2012.

K. Kaczmarczyk, A. Wit, M. Krawczyk, and J. Zaborski, “Gait classification in post-stroke patients using artificial neural networks,” Gait Posture, vol. 30, no. 2, pp. 207–210, 2009.

N. M. Tahir and H. H. Manap, “Parkinson disease gait classification based on machine learning approach,” Journal of Applied Science, vol. 12, no. 2. pp. 180–185, 2012.

C. Z. C. Hasan, R. Jailani, and N. M. Tahir, “ANN and SVM Classifiers in Identifying Autism Spectrum Disorder Gait Based on Three-Dimensional Ground Reaction Forces,” in TENCON 2018 - 2018 IEEE Region 10 Conference, 2018, no. October, pp. 2436–2440.

C. Z. C. Hasan, R. Jailani, and N. M. Tahir, “Automated Classification of Gait Abnormalities in Children with Autism Spectrum Disorders Based on Kinematic Data,” Int. J. Psychiatry Psychother., vol. 2, pp. 10–15, 2017.

C. Z. C. Hasan, R. Jailani, N. M. Tahir, and R. Sahak, “Autism spectrum disorders gait identification using ground reaction forces,” TELKOMNIKA, vol. 15, no. 2, pp. 903–911, 2017.

S. Ilias, N. M. Tahir, R. Jailani, and C. Z. C. Hasan, “Linear discriminant analysis in classifying walking gait of autistic children,” in 11th European Modelling Symposium on Computer Modelling and Simulation, EMS 2017, 2017, pp. 67–72.

C. Z. C. Hasan, R. Jailani, and N. M. Tahir, “Use of statistical approaches and artificial neural networks to identify gait deviations in children with autism spectrum disorder,” Int. J. Biol. Biomed. Eng., vol. 11, pp. 74–79, 2017.

R. Best and R. Begg, “Overview of movement analysis and gait features,” in Computational intelligence for movement sciences: neural networks and other emerging techniques, London, UK: IGI Global, 2006.

S. M. Reid, R. B. Graham, and P. A. Costigan, “Differentiation of young and older adult stair climbing gait using principal component analysis,” Gait Posture, vol. 31, no. 2, pp. 197–203, 2010.

B. Henderson et al., “Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models,” 2020 31st Irish Signals Syst. Conf. ISSC 2020, no. September, 2020.



  • There are currently no refbacks.

Copyright (c) 2023 Che Zawiyah Che Hasan, Rozita Jailani, Nooritawati Md Tahir

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License