Healthcare Event Aggregation Lab (HEAL), a knowledge sharing platform for anomaly detection and prediction

摘要

Due to the increase in elderly population, research in healthcare monitoring and ambient assisted living technology is crucial to provide improved care and at the same time contain the healthcare cost. Among existing systems, there is none robust system that can act as a bridge between different systems to facilitate knowledge sharing, so as to empower the detection and prediction capabilities of them. These systems cannot use the data and knowledge of other similar systems due to the complexity involved in sharing data between them. Storing the information is also a challenge due to a high volume of sensor data generated by each sensor. The proposed HEAL model is a platform that provides services to developers to leverage the previously processed similar data and the corresponding detected symptoms. The proposed architecture is cloud-based and provides services for input sensors, Internet of Things devices, and context providers. The ultimate goal of the system is to fill the gap between symptoms and diagnosis trend data in order to predict health anomalies accurately and quickly.