用事件流训练的智能家庭行为监测基础模型，可跨环境通用。

Smart-home sensor-based behavioral monitoring holds significant potential for healthcare, independent living, and early detection of functional or cognitive changes. In this setting, tasks like activity recognition, prediction, and pattern discovery provide complementary views of daily life, supporting the modeling of personal routines and habits, and their long-term changes. Existing approaches, however, face critical limitations. Supervised approaches require impractical amounts of labeled activity data to capture the variability of daily behavior across residents and environments. Foundation models represent a promising direction for learning transferable representations of latent behavioral patterns from sensor data. Still, current efforts are mostly designed for continuous inertial or physiological sensor data and do not address the sparse, discrete, and semantically rich event streams produced by smart homes. In this paper, we introduce DomusFM, a domain-specific foundation model for sensor-based behavioral monitoring in smart homes based on semantic event streams. DomusFM employs a self-supervised dual contrastive learning paradigm to capture both event-level semantic attributes and sequence-level temporal dependencies. By integrating semantic embeddings from a lightweight language model and specialized encoders for temporal patterns and binary states, DomusFM learns transferable representations that can be adapted across heterogeneous smart-home environments and tasks related to activity and event analysis. Through a leave-one-dataset-out evaluation across seven public smart-home datasets, we demonstrate that DomusFM consistently outperforms baselines on three downstream tasks: ADL recognition, next-k event prediction, and unsupervised clustering. DomusFM has a small footprint and can be deployed on edge devices.