在ASD中增加步态的时间变化:动作捕捉和机器学习分析
Katharine Goldthorp1, Benn Henderson2, Pratheepan Yogarajah2
1School of Psychology and Sports Science, Bangor University, Bangor LL57 2DG, UK.
Biology
|July 29, 2025
概括
自闭症谱系障碍 (ASD) 与非典型的步态有关. 时间步态分析和机器学习可以将自闭症患者与典型发育的同龄人区分开来,这表明它有可能作为诊断辅助工具.
科学领域:
- 神经科学是一个神经科学.
- 生物机械工程 生物机械工程
- 发展心理学 发展心理学
背景情况:
- 在患有自闭症谱系障碍 (ASD) 的人群中,经常观察到运动缺陷,特别是非典型的步态.
- 在ASD中走路差异的潜在机制和精确特征仍然不完全理解.
- 步态计时为探索运动差异提供了一个可衡量和可访问的指标.
研究的目的:
- 调查时间步行参数是否可以单独描述自闭症步行的特征.
- 确定通过机器学习增强的时间步行分析是否可以作为ASD和典型发展 (TD) 个体之间的分类器.
- 探索步行时间分析作为ASD诊断工具的潜力.
主要方法:
- 对两组男性参与者进行了高分辨率的步态时间分析:高功能ASD (N=16) 和TD (N=16),年龄为735岁.
- 数据采集使用VICON® 3D运动分析系统.
- 机器学习模型,包括随机森林,应用于时间步态变化数据进行分类.
主要成果:
- 与TD组相比,ASD组在所有测试的步态参数中表现出显著增加的时间变化 (p < 0.001).
- 机器学习分析表明,时间步态变化有效地将参与者分为ASD和TD组.
- 随机森林在12个测试的算法中出现了表现最好的模型.
结论:
- 时间步行分析揭示了ASD和TD个体之间的步行时间变化存在显著差异.
- 机器学习算法可以有效地利用步态时间变化来进行组分类.
- 这种方法有望成为未来潜在的ASD诊断辅助工具.
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