Bridging Objective and Subjective Data: A Comprehensive Architecture for Wearable Health Research

This study presents a secure, end-to-end architecture for multimodal health data collection and integration using a multisensor wearable device (Empatica EmbracePlus) and a mobile health application (FoTiP). The system captures continuous, objective data as well as subjective data, by integrating these streams with a backend data-collection framework (mSpider). Beyond the architecture design, the successful integration of Empatica with mSpider demonstrates efficient data retrieval, tag management, and storage, facilitating the creation of comprehensive datasets for health-event analysis. This framework advances personalized healthcare by supporting predictive modeling for health events such as migraine episodes, epileptic seizures and stress while also analyzing behavioral and lifestyle patterns for more informed interventions.