通过远程对话分析面部、语音等特征，可有效评估老年人认知与心理状态。
Feasibility of Detecting Cognitive Impairment and Psychological Well-being among Older Adults Using Facial, Acoustic, Linguistic, and Cardiovascular Patterns Derived from Remote Conversations
- 从视频通话中提取多模态特征，用机器学习建模评估健康状况。
- 识别轻度认知障碍（CDR 0.5）准确率达AUC 0.77，心理状态预测最高AUC 0.75。
- 语音语言特征对认知评估更有效，面部与心率特征反映心理状态，适合老年健康监测。
老龄化社会亟需可扩展的方法来监测认知衰退，并识别与痴呆风险相关的社交与心理因素。本研究基于39名正常认知或轻度认知障碍（MCI）老年人的远程视频对话数据，提取面部、声学、语言及心血管特征，量化其认知状态、社会孤立、神经质和心理幸福感。模型在区分临床痴呆量表（CDR）0.5（vs. 0）时达到0.77 AUC，社会孤立预测为0.74 AUC，社会满意度0.75 AUC，心理幸福感0.72 AUC，负性情绪0.74 AUC。特征重要性分析表明，语言模式有助于认知评估，而面部表情与心血管模式更适用于心理状态判断。偏倚分析显示，模型在年龄、性别、疾病状况和教育水平方面存在显著偏差。研究证明了利用远程对话进行老年人认知与心理健康的可行性，强调需构建大规模跨人群访谈数据集，以推动深度学习模型在多样化背景中的泛化应用。
原文摘要 · Abstract (English)
The aging society urgently requires scalable methods to monitor cognitive decline and identify social and psychological factors indicative of dementia risk in older adults. Our machine learning (ML) models captured facial, acoustic, linguistic, and cardiovascular features from 39 older adults with normal cognition or Mild Cognitive Impairment (MCI), derived from remote video conversations and quantified their cognitive status, social isolation, neuroticism, and psychological well-being. Our model could distinguish Clinical Dementia Rating Scale (CDR) of 0.5 (vs. 0) with 0.77 area under the receiver operating characteristic curve (AUC), social isolation with 0.74 AUC, social satisfaction with 0.75 AUC, psychological well-being with 0.72 AUC, and negative affect with 0.74 AUC. Our feature importance analysis showed that speech and language patterns were useful for quantifying cognitive impairment, whereas facial expressions and cardiovascular patterns were useful for quantifying social and psychological well-being. Our bias analysis showed that the best-performing models for quantifying psychological well-being and cognitive states in older adults exhibited significant biases concerning their age, sex, disease condition, and education levels. Our comprehensive analysis shows the feasibility of monitoring the cognitive and psychological health of older adults, as well as the need for collecting largescale interview datasets of older adults to benefit from the latest advances in deep learning technologies to develop generalizable models across older adults with diverse demographic backgrounds and disease conditions.
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