用可穿戴设备追踪老年人行为与健康，发现长期数据能显著提升预测效果。
Longitudinal Multimodal Sensing of Physical Activity and Well-Being in Older Adults
- 构建真实世界中66位老人的多模态长期监测数据集
- 行为目标预测准确率最高达宏平均F1 65%
- 历史数据是关键，尤其对抽象健康指标有重要价值
可穿戴和移动传感技术使得在真实环境中持续监测人类行为与健康成为可能。然而，针对复杂或临床定义结果的纵向多模态数据预测建模仍具挑战性。本研究在真实世界条件下对66名老年人开展纵向多模态研究，整合可穿戴设备、行为监测与临床评估。该设置为研究这一代表性不足人群提供了罕见机会。基于此数据集，我们探究感知信号与目标变量对齐程度对预测性能的影响。设计了一个涵盖从易观测到难观测任务的统一评估框架，包括活动水平预测、睡眠时长估计和睡眠呼吸暂停严重程度分类。结果显示预测能力呈清晰梯度：高可观测行为目标表现稳健（宏平均F1 65%），而更抽象的结局虽优于基线模型但仍具挑战性。通过可解释性分析发现，历史特征始终是最具信息量的预测因子，凸显纵向信息的核心作用。
原文摘要 · Abstract (English)
Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings. However, predictive modeling in longitudinal multimodal data remains challenging, particularly when targeting complex or clinically derived outcomes. In this work, we present a longitudinal multimodal study of 66 older adults conducted in real-world conditions and combining wearable sensing, behavioral monitoring, and clinical assessments. This setting provides a rare opportunity to study an underrepresented population in long-term, into-the-wild conditions. Building on this dataset, we investigate how the alignment between sensed signals and target variables affects predictive performance across health-related tasks. We design a unified evaluation framework spanning tasks with increasing levels of observability, including Activity Levels prediction, Sleep Duration estimation, and Sleep Apnea Severity classification. Our results reveal a clear gradient of predictability: highly observable behavioral targets achieve robust performance (macro-F1 65%), while more abstract outcomes remain challenging despite consistent improvements over baseline models. Moreover, through explainability analysis, we show that historical features consistently emerge as the most informative predictors, highlighting the central role of longitudinal information.
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