Cluster-LLM: Clustering-Guided LLM-Based Data Augmentation for Anomaly Detection in Smart Home

Anomaly detection in activities of daily living (ADL) within smart home environments is critical for supporting the safety and well-being of older adults living independently. However, the scarcity of annotated anomaly data and the inherent behavioral heterogeneity among residents pose significant challenges to developing robust detection models. To address these issues, we propose a novel clustering-guided LLM-based data augmentation framework (Cluster-LLM) for ADL anomaly detection. This framework first leverages clustering algorithms to mine differential behavioral patterns from raw sensor data, and then generates lifelike synthetic activity sequences matching the characteristics of each behavioral group through LLM reasoning. We conduct extensive evaluations on four clustering approaches, namely K-means, K-Medoids, DBSCAN and deep embedded clustering (DEC), using two standard smart home datasets. Extensive experiments with four one-class classifiers and seven evaluation metrics demonstrate that Cluster-LLM achieves satisfactory performance in anomaly detection in smart home. Specifically, Cluster-LLM boosts the best F1-score on the Kyoto7 dataset from 0.444 to 0.667, representing a 50.2% relative improvement over baselines. On the unsupervised benchmark, the proposed framework enhances accuracy across diverse clustering backbones, while maintaining robust F1 gains within a moderate augmentation scale.