Spatio-temporal graph neural networks achieve strong traffic forecasting accuracy, yet their robustness under out-of-distribution (OOD) conditions, such as traffic incidents, remains poorly understood. We propose SRGNet, a spectrally-regularized graph network combining three targeted innovations: (1) spectral normalization on all weight matrices to bound the Lipschitz constant; (2) disruption-aware training augmentation synthesizing incident-like flow drops; and (3) stochastic depth creating an implicit ensemble. We evaluate on PEMS-BAY using an impact-verified OOD protocol with 996 real incidents ( 30% flow reduction). SRGNet achieves the lowest OOD degradation (+116.0%) among competitive models, the best local OOD RMSE (0.987) at the most-impacted sensors, and a standard RMSE of 0.3123, demonstrating the best accuracy–robustness tradeoff.
Huu Dang Khoi Nguyen, T. Le· E3S Web of Conferences· 0 citations
Smart home environments provide a practical basis for privacy-preserving activity monitoring in elderly-care and independent living settings, where ambient sensors such as motion detectors and door contacts can observe daily routines without requiring any action or device from residents. However, ambient-sensor-based human activity recognition (HAR) faces a persistent challenge: fine-grained activity labels commonly used in benchmark datasets, such as Cook Breakfast, Cook Lunch, and Cook Dinner, often exceed the discriminative capacity of sparse environmental sensors, introducing label ambiguity and fragmenting the training data available per class. To address this issue, this paper proposes a sensor-aligned activity taxonomy that reorganizes activity classes according to sensor distinguishability rather than semantic granularity. The taxonomy is integrated with temporal feature engineering and a personalized LightGBM-based recognition pipeline optimized for edge deployment. Experiments on 25 households from the CASAS smart home dataset show that the proposed approach improves mean recognition accuracy from 74.83% to 82.45% and increases the F1-score for the clinically relevant activity Take Medicine from 0.42 to 0.61. These results suggest that aligning activity label design with sensor observability can meaningfully improve recognition accuracy without increasing model complexity.
Le Bao Ngoc Tran, Duc Dat Pham, Huu-Sy Le et al.· 2026 11th International Conf...· 0 citations
