Toward Elderly-Care-Oriented Smart Home HAR via Sensor-Aligned Activity Taxonomy

Abstract

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.