A health telemonitoring platform based on data integration from different sources
Gianluigi Ciocca, Paolo Napoletano, Matteo Romanato, Raimondo, Schettini

TL;DR
This paper presents a telemonitoring platform that integrates data from various consumer and custom devices, uses machine learning for data processing, and provides an accessible dashboard to support long-term health management.
Contribution
The novel platform integrates multiple data sources and devices, leveraging machine learning and user-friendly interfaces to improve chronic disease monitoring.
Findings
Good user satisfaction in preliminary usability tests
Effective integration of consumer and custom health devices
Real-time monitoring with machine learning enhances health management
Abstract
The management of people with long-term or chronic illness is one of the biggest challenges for national health systems. In fact, these diseases are among the leading causes of hospitalization, especially for the elderly, and huge amount of resources required to monitor them leads to problems with sustainability of the healthcare systems. The increasing diffusion of portable devices and new connectivity technologies allows the implementation of telemonitoring system capable of providing support to health care providers and lighten the burden on hospitals and clinics. In this paper, we present the implementation of a telemonitoring platform for healthcare, designed to capture several types of physiological health parameters from different consumer mobile and custom devices. Consumer medical devices can be integrated into the platform via the Google Fit ecosystem that supports hundreds of…
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Taxonomy
TopicsMobile Health and mHealth Applications
MethodsDiffusion
