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用人工智能辅助的注意力控制建模,用于干预参与

Yang Merik Liu, Adam Turnbull, Meishan Ai

bioRxiv : the preprint server for biology

|August 6, 2025

概括

一个新的AI工具,使用数字测量 (AID) 的注意力控制指数,使用面部表情和心率变化来跟踪老年人参与认知训练的参与,提高干预效率.

科学领域:

老年学是一门学科.
认知神经科学认知神经科学
人工智能的人工智能

背景情况:

认知训练旨在防止老年人认知能力下降,但结果不一致.
缺乏有效的参与是认知训练有效性的关键限制.
客观测量注意力参与是个性化干预的必要条件.

研究的目的:

开发和验证一种新的AI辅助多式联络框架 (AID),用于在认知训练期间评估注意力控制.
结合面部表情分析和心率变化 (HRV) 来进行参与评估.
预测老年人的行为参与和认知结果.

主要方法:

使用数字测量 (AID) 框架开发了注意力控制指数.
综合视频基面部表情分析与心电图衍生的心率变化 (HRV).
使用两组独立的老年人认知障碍风险数据集验证了AID.

主要成果:

AID准确地预测了参与和认知结果的行为标记.
识别了特定的面部表情和HRV特征,表明注意力控制.
在数据集中证明了AID的稳定性和通用性.

结论:

在认知训练中,AID提供了一个强大的,可通用的数字标记器,用于评估认知训练中的注意控制.
这个框架可以通过专注于实时的个人用户动态来增强参与度.
未来的应用可能涉及优化认知训练,以改善老年人的结果.

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