Audio-Driven Multi-Modal Unobtrusive Health Monitoring and Inference for Smart Eldercare at Home

As the aging population grows and more elderly individuals live independently, the demand for reliable, unobtrusive home health monitoring becomes increasingly important. Existing in-home health monitoring systems often face limitations such as privacy concerns, dependence on unreliable wearable devices, degraded accuracy in complex environments, and lack of continuous monitoring capability. To address these challenges, we propose a long-term home health monitoring system that primarily relies on audio sensing, supplemented by other noninvasive modalities. Our approach is able to accurately detect and recognize overlapping acoustic events with fine-grained temporal resolution, surpassing conventional audio-based methods for activity recognition. The system incorporates a transformer-based time-frequency fusion module and a category dynamic threshold strategy to improve detection performance under semi supervised conditions. Experiments on real-world dataset demonstrate that our method outperforms existing baselines, achieving PSDS$_{1}$, PSDS$_{2}$, and EB-F1 scores of 0.581, 0.930 and 55.1%, with improvements of 0.054, 0.019, and 2.3%, respectively. In addition, a 30 day field deployment involving 10 elderly participants confirms the robustness and practicality of the system for real-world applications. By allowing continuous passive monitoring of daily activities and abnormal acoustic events, our system has significant potentials for early detection of health risks, behavioral anomalies, and long-term wellness tracking in aging in place scenarios.