HealthSense: classification of health-related sensor data through user-assisted machine learning
Proceedings of the 9th workshop on Mobile computing systems and applications
Real-time analysis of physiological data to support medical applications
IEEE Transactions on Information Technology in Biomedicine
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Advances in technology allow the continuous physiological monitoring of people using noninvasive sensors. An important issue in this context is the real-time analysis of physiological signals performed on mobile devices, which requires optimized power consumption and short processing response time. The SAPhyRA framework performs real-time stream analysis for physiological risk assessment. To this aim, the framework evaluates people's health conditions by analyzing different clinical signals in a sliding time window. Given historical physiological measures, different models of patients and diseases are built. The most suitable model for the current monitored patient is exploited in the real time stream classification phase. Preliminary experiments performed on public physiological data show the effectiveness and flexibility of the proposed approach.