Smart homes equipped with ambient sensors offer a transformative approach to continuous health monitoring and assisted living. Traditional research in this domain primarily focuses on Human Activity Recognition (HAR), which relies on mapping sensor data to a closed set of predefined activity labels. However, the fixed granularity of these labels often constrains their practical utility, failing to capture the subtle, household-specific nuances essential, for example, for tracking individual health over time. To address this, we propose DISCOVER, a framework for discovering and annotating Patterns of Daily Living (PDL) - fine-grained, recurring sequences of sensor events that emerge directly from a resident's unique routines. DISCOVER utilizes a self-supervised feature extraction and representation-aware clustering pipeline, supported by a custom visualization interface that enables experts to interpret and label discovered patterns with minimal effort. Our evaluation across multiple smart-home environments demonstrates that DISCOVER identifies cohesive behavioral clusters with high inter-rater agreement while achieving classification performance comparable to fully-supervised baselines using only 0.01% of the labels. Beyond reducing annotation overhead, DISCOVER establishes a foundation for longitudinal analysis. By grounding behavior in a resident's specific environment rather than rigid semantic categories, our framework facilitates the observation of within-person habitual drift. This capability positions the system as a potential tool for identifying subtle behavioral indicators associated with early-stage cognitive decline in future longitudinal studies.

翻译：配备环境传感器的智能家居为持续健康监测与辅助生活提供了变革性方法。该领域的传统研究主要集中于人体活动识别（HAR），其依赖于将传感器数据映射到一组封闭的预定义活动标签。然而，这些标签的固定粒度往往限制了其实用性，无法捕捉对长期追踪个体健康至关重要的、具有家庭特异性的细微差异。为此，我们提出DISCOVER框架，用于发现和标注日常生活模式（PDL）——即直接从住户独特日常习惯中涌现的、细粒度的、重复出现的传感器事件序列。DISCOVER采用自监督特征提取与表征感知聚类流程，并辅以定制可视化界面，使专家能够以最小工作量解读和标注发现的模式。我们在多种智能家居环境中的评估表明，DISCOVER能够识别具有高评分者一致性的内聚行为簇，同时在使用仅0.01%标签量的情况下达到与全监督基线相当的分类性能。除降低标注成本外，DISCOVER为纵向分析奠定了基础。通过将行为锚定在住户特定环境中而非僵化的语义类别，我们的框架有助于观察个体内部习惯性漂移。该能力使本系统有望成为未来纵向研究中识别与早期认知衰退相关的细微行为指标的潜在工具。