用手机数据检测老人认知衰退，提升准确率。
Deep Learning-Based Detection of Cognitive Impairment from Passive Smartphone Sensing with Routine-Aware Augmentation and Demographic Personalization
- 用行为序列建模+相似日替换生成新数据增强泛化
- 结合年龄性别等特征重加权训练，AUPRC升至0.766
- 适合做老龄化人群的长期认知健康监测
早期发现认知障碍对及时诊断和干预至关重要，但传统临床评估频率低，难以捕捉老年人细微的认知退化。被动式智能手机传感为自然状态下的持续认知监测提供了新途径。基于此，我们构建了一个长短期记忆（LSTM）模型，利用一项为期一年的老年群体研究中采集的多模态传感数据，提取每日行为特征序列，用于识别认知障碍。核心贡献是两项提升模型跨个体泛化能力的技术：(1) 基于日常规律的增强方法，通过用行为相似的替代日生成合成数据序列；(2) 人口统计个性化方法，对与测试对象人口学特征相近的训练样本赋予更高权重。在36名老年人6个月的数据上评估，结合这两项技术后，基于传感与人口统计特征的模型AUPRC从0.637提升至0.766，展现了被动传感在老年群体中实现可扩展认知监测的巨大潜力。
原文摘要 · Abstract (English)
Early detection of cognitive impairment is critical for timely diagnosis and intervention, yet infrequent clinical assessments often lack the sensitivity and temporal resolution to capture subtle cognitive declines in older adults. Passive smartphone sensing has emerged as a promising approach for naturalistic and continuous cognitive monitoring. Building on this potential, we implemented a Long Short-Term Memory (LSTM) model to detect cognitive impairment from sequences of daily behavioral features, derived from multimodal sensing data collected in an ongoing one-year study of older adults. Our key contributions are two techniques to enhance model generalizability across participants: (1) routine-aware augmentation, which generates synthetic sequences by replacing each day with behaviorally similar alternatives, and (2) demographic personalization, which reweights training samples to emphasize those from individuals demographically similar to the test participant. Evaluated on 6-month data from 36 older adults, these techniques jointly improved the Area Under the Precision-Recall Curve (AUPRC) of the model trained on sensing and demographic features from 0.637 to 0.766, highlighting the potential of scalable monitoring of cognitive impairment in aging populations with passive sensing.
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