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Virtual Reality Tools for Assessing Unilateral Spatial Neglect: A Novel Opportunity for Data Collection

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Published on: March 10, 2021

使用机器学习模型和虚拟现实评估自闭症的生物信号比较.

Maria Eleonora Minissi¹, Alberto Altozano¹, Javier Marín-Morales¹

¹Instituto Universitario de Investigación en Tecnología Centrada en El Ser Humano (HUMAN-tech), Universitat Politécnica de Valencia, Valencia, Spain.

Computers in biology and medicine

|March 1, 2024

概括

对于客观自闭症谱系障碍 (ASD) 评估,运动技能显示出希望. 这项研究发现,使用虚拟现实的运动技能,比眼睛运动或行为反应更可靠地识别ASD.

关键词:

自闭症谱系障碍自闭症谱系障碍生物信号是一种生物信号.眼睛的运动眼睛的运动运动技巧运动技巧统计机器学习的统计虚拟现实虚拟现实就是虚拟现实.

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科学领域:

计算精神病学是一种计算精神病学.
神经发育障碍神经发育障碍
生物医学工程生物医学工程

背景情况:

对自闭症谱系障碍 (ASD) 的临床评估往往缺乏客观性,因为依赖主观数据.
计算精神病学和虚拟现实 (VR) 提供了在生态环境中基于生物标志物的客观评估的潜力.
现有的ASD研究缺乏对生物信号进行系统的比较,以便在同时的生态记录中进行自动分类.

研究的目的:

为了比较不同生物信号的有效性,在虚拟现实环境中自动分类ASD.
评估基于运动技能,眼睛运动和行为反应的机器学习模型,以检测ASD.
评估基于生物信号的评估在识别ASD时的稳定性和可靠性.

主要方法:

开发和比较机器学习模型使用隐性 (运动技能,眼睛运动) 和显式 (行为反应) 生物信号记录在VR.
利用一个VR选工具,用四个不同的虚拟场景来收集数据.
采用线性支向量分类器,具有递归特征消除,通过嵌套交叉验证进行验证.

主要成果:

基于运动技能的机器学习模型在ASD识别中表现出了最高的稳定性,实现了0.89的曲线下面面积 (AUC) (SD = 0.08).
表现最好的行为反应模型实现了0.80.0的AUC.
眼动模型显示出局限性,需要进一步研究,因为眼睛跟踪眼镜的问题.

结论:

运动技能是提高ASD早期评估客观性和可靠性的一个非常有希望的生物信号.
基于VR的评估工具整合了运动技能分析,比传统的主观方法有潜在的进步.
眼睛跟踪技术需要进一步开发才能在VR.ASD评估中有效地利用眼睛跟踪技术.