Machine learning's effectiveness in evaluating movement in one-legged standing test for predicting high autistic
Yoshimasa Ohmoto1, Kazunori Terada2, Hitomi Shimizu3
1Department of Behavior Informatics, Faculty of Informatics, Shizuoka University, Shizuoka, Japan.
Frontiers in Psychiatry
|November 1, 2024
Summary
Machine learning effectively predicts high autistic traits using the one-legged standing test (OLST) and movement analysis. Shoulder and trunk movements are key indicators for identifying autistic traits in children.
Area of Science:
- Neuroscience
- Developmental Psychology
- Biomedical Engineering
Background:
- Growing evidence links autism spectrum disorder (ASD) to motor impairments, particularly balance deficits.
- The one-legged standing test (OLST) is a common assessment for balance.
- Machine learning (ML) offers objective analysis of movement data for clinical assessments.
Purpose of the Study:
- To evaluate the efficacy of ML in analyzing OLST movement data for predicting high autistic traits.
- To identify key movement variables from OLST that are predictive of autistic traits.
Main Methods:
- 126 children (64 boys, 62 girls) performed the OLST on a pressure sensor.
- Full-body images and foot pressure data were collected.
- A Support Vector Machine (SVM) algorithm was employed to classify participants into high and low autistic trait groups.
Main Results:
- ML models achieved perfect accuracy (1.000) for sensitivity and specificity using proposed and combined variables.
- Shoulder, hip, and trunk movements were identified as significant predictors of balance status in children with high autistic traits.
- Higher Social Responsiveness Scale scores correlated with increased probability of belonging to the high autistic trait group.
Conclusions:
- ML analysis of OLST movement data, particularly shoulder and waist movements, is effective for predicting high autistic traits.
- Further research incorporating diverse balance metrics is recommended to enhance predictive capabilities.
