The recognition of Activities of Daily Living (ADLs) from event-triggered ambient sensors is an essential task in Ambient Assisted Living, yet existing methods remain constrained by representation-level limitations. Sequence-based approaches preserve temporal order of sensor activations but are sensitive to noise and lack spatial awareness, while image-based approaches capture global patterns and implicit spatial correlations but compress fine-grained temporal dynamics and distort sensor layouts. Naïve fusion (e.g., feature concatenation) fails to enforce alignment between sequence- and image-based representation views, underutilizing their complementary strengths. We propose Contrastive Alignment for ADL Recognition from Event-Triggered Sensor Streams (CARE), an end-to-end framework that jointly optimizes representation learning via Sequence–Image Contrastive Alignment (SICA) and classification via cross-entropy, ensuring both cross-representation alignment and task-specific discriminability. CARE integrates (i) time-aware, noise-resilient sequence encoding with (ii) spatially-informed and frequency-sensitive image representations, and employs (iii) a joint contrastive-classification objective for end-to-end learning of aligned and discriminative embeddings. Evaluated on three CASAS datasets, CARE achieves state-of-the-art performance (89.8% on Milan, 88.9% on Cairo, and 73.3% on Kyoto7) and demonstrates robustness to sensor malfunctions and layout variability, highlighting its potential for reliable ADL recognition in smart homes. We release our code at https://github.com/Jhziiiig/CARE.

Smart homes equipped with ambient sensors enable privacy conscious monitoring of Activities of Daily Living (ADLs), but produce event-triggered sensor logs that differ fundamentally from regularly sampled time series. These data are discrete, symbolic, irregular, and spatially grounded, exhibiting strong structural dependencies across sensor states and locations. Collecting sufficiently diverse labeled data with adequate structural coverage remains challenging, motivating the need for realistic synthetic data generation. Existing time-series generation methods are designed for continuous or regularly sampled temporal signals and are poorly aligned with smart-home sensor data, where realism fundamentally requires jointly preserving coherent statistical patterns, physical feasibility, and activity-level semantic consistency. We propose ADLGen, a unified framework for synthesizing symbolic, event-triggered sensor sequences. ADLGen integrates a representation tailored to symbolic event data, a generation process that enforces spatial constraints while balancing diversity and coherence, and an LLM-based semantic evaluation and refinement stage for identifying and correcting behavioral inconsistencies, with efficient offline deployment through compact local models. Experiments show that ADLGen synthesizes sequences that closely match real data while improving intrinsic realism, downstream activity recognition, rare-activity learning, and cross-home generalization over strong baselines.