DataSheet1_Machine learning’s effectiveness in evaluating movement in one-legged standing test for predicting high autistic trait.docx

IntroductionResearch 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.
MethodsIn 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.
ResultsFor 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.
DiscussionResults 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.

引言
越来越多的研究证实，自闭症谱系障碍（Autism Spectrum Disorder, ASD）儿童存在包括平衡协调能力受损在内的多种运动功能障碍。单腿站立测试（One-Legged Standing Test, OLST）是一种广泛应用的平衡能力评估手段。由于机器学习是一种可从运动数据中学习预测模型的强大技术，能够客观分析单腿站立测试中的相关运动过程，本研究旨在评估机器学习方法在单腿站立测试运动数据分析中对高自闭症特质群体的预测效能。

方法
本研究共招募64名男性与62名女性参与者。实验要求参与者面向实验者，单腿站立于压力传感器上。实验采集的数据包括足底压力分布时序数据与全身图像数据。本研究采用支持向量机（Support Vector Machine, SVM）算法，以16个解释变量构建分类模型，用于区分高自闭症特质组与低自闭症特质组参与者。此外，分别针对传统解释变量、本研究提出的解释变量以及二者组合的变量类别构建分类模型，并通过SVM模型计算样本归属高自闭症特质组的概率。

结果
针对本研究提出的变量与组合变量，模型的准确率、灵敏度与特异度均达到1.000。肩部、髋部与躯干的运动变量为关键特征，可解释高自闭症特质儿童的平衡状态。此外，社交反应量表（Social Responsiveness Scale, SRS）总分与各解释变量类别下的高自闭症特质组归属概率呈正相关。

讨论
研究结果表明，结合运动影像与机器学习方法分析单腿站立测试的运动数据，可有效预测高自闭症特质群体。同时，研究结果凸显了肩部与腰部运动在高效预测高自闭症特质中的重要意义。最后，未来研究需纳入更广泛的平衡相关线索，以全面评估利用平衡能力预测高自闭症特质的应用效能。