D3.483 - Wearable Energy-efficient Device development for Real-time Evaluation, Analysis, and Monitoring platform

Allergic and environmentally related diseases are strongly influenced by rapidly changing exposures, yet fixed-site monitoring often fails to capture person-specific variability driven by mobility and daily activity patterns. In this study, we aim to support prevention and early intervention by linking individualized exposure estimates with respiratory and skin-related biomarkers through a receptor-centered exposure–health assessment framework.Allergic and environmentally related diseases are strongly influenced by rapidly changing exposures, yet fixed-site monitoring often fails to capture person-specific variability driven by mobility and daily activity patterns. In this study, we aim to support prevention and early intervention by linking individualized exposure estimates with respiratory and skin-related biomarkers through a receptor-centered exposure–health assessment framework.

In this study, the platform integrates GPS-based personal location tracking data, fine-grained environmental data obtained via external APIs, and wearable biosignals. The platform manages key environmental variables and aligns them in time with biosignal streams for analysis. Health monitoring includes lung-sound event detection/counting and skin-state monitoring focused on hydration-related measurements, supported by a wearable skin-sensing patch for convenient, repeated skin sensing in daily life. AI pipelines perform signal-quality screening and respiratory sound classification, with outputs stored and visualized through unified dashboards.

The platform provides integrated summaries of time-resolved personal exposures together with lung-sound events and skin measurements, including patch-based skin measurements. A real-time abnormal lung-sound counter was applied to clinical lung-sound recordings and showed agreement with clinician interpretation. In addition, synchronized management of environmental data, location information, and biosignals established a basis for longitudinal tracking and time-aligned analysis of symptom-relevant signal fluctuations in relation to changing personal exposures.

This study presents an end-to-end wearable–AI–platform framework that time-aligns individualized environmental exposures with respiratory and skin-related biomarkers, supporting scalable monitoring and early-warning services for environmentally triggered allergic disease activity. Future work will strengthen clinical validity through standardized reference datasets and prospective evaluations using clinically meaningful metrics.