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ORIGINAL RESEARCH article

Front. Psychiatry
Sec. Autism
Volume 15 - 2024 | doi: 10.3389/fpsyt.2024.1464285
This article is part of the Research Topic Autism: The Movement (Sensing) Perspective a Decade Later View all 20 articles

Machine Learning's Effectiveness in Evaluating Movement in One-Legged Standing Test for predicting high autistic trait

Provisionally accepted
Yoshimasa Ohmoto Yoshimasa Ohmoto 1Kazunori Terada Kazunori Terada 2Hitomi Shimizu Hitomi Shimizu 3Hiroko Kawahara Hiroko Kawahara 3Ryoichiro Iwanaga Ryoichiro Iwanaga 3Hirokazu Kumazaki Hirokazu Kumazaki 3*
  • 1 Shizuoka University, Shizuoka, Shizuoka, Japan
  • 2 Gifu University, Gifu, Gifu, Japan
  • 3 Nagasaki University, Nagasaki, Japan

The final, formatted version of the article will be published soon.

    Research supporting the presence of diverse motor impairments, including impaired balance coordination, in children with autism spectrum disorder (ASD) is increasing. The one-legged standing test (OLST) is a popular test of balance. Since machine learning is a powerful technique for learning predictive models from movement data, it can objectively evaluate the processes involved in OLST. This study assesses machine learning's effectiveness in evaluating movement in OLST for predicting high autistic trait. Methods: In this study, 64 boys and 62 girls participated. The participants were instructed to stand on one leg on a pressure sensor while facing the experimenter. The data collected in the experiment were time-series data pertaining to pressure distribution on the sole of the foot and full-body images. A model to identify the participants belonging to High autistic trait group and Low autistic trait group was developed using a support vector machine (SVM) algorithm with 16 explanatory variables. Further, classification models were built for the conventional, proposed, and combined explanatory variable categories. The probabilities of High autistic trait group were calculated using the SVM model. Results: For proposed and combined variables, the accuracy, sensitivity, and specificity scores were 1.000. The variables shoulder, hip, and trunk are important since they explain the balance status of children with high autistic trait. Further, the total Social Responsiveness Scale score positively correlated with the probability of High autistic trait group in each category of explanatory variables. Discussion: Results indicate the effectiveness of evaluating movement in OLST by using movies and machine learning for predicting high autistic trait. In addition, they emphasize the significance of specifically focusing on shoulder and waist movements, which facilitate the efficient predicting high autistic trait. Finally, studies incorporating a broader range of balance cues are necessary to comprehensively determine the effectiveness of utilizing balance ability in predicting high autistic trait.

    Keywords: Autistic trait, machine learning, balance, One-legged standing, screening

    Received: 13 Jul 2024; Accepted: 30 Sep 2024.

    Copyright: © 2024 Ohmoto, Terada, Shimizu, Kawahara, Iwanaga and Kumazaki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hirokazu Kumazaki, Nagasaki University, Nagasaki, Japan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.