Sensor Data Streams Correlation Platform for Asthma Management

Asthma is a high-burden chronic inflammatory disease with prevalence in children with twice the rate compared to adults. It can be improved by continuously monitoring patients and their environment using the Internet of Things (IoT) based devices. These sensor data streams so obtained are essential to comprehend multiple factors triggering asthma symptoms. In order to support physicians in exploring causal associations and finding actionable insights, a visualization system with a scalable cloud infrastructure that can process multimodal sensor data and Patient Generated Health Data (PGHD) is necessary. In this thesis, we describe a cloud-based asthma management and visualization platform that integrates personalized PGHD from kHealth kit and outdoor environmental observations from web services. When applied to data from an individual, the tool assists in analyzing and explaining symptoms using personalized causes, monitor disease progression, and improve asthma management. The front-end visualization was built with Bootstrap Framework and Highcharts. Google\u27s Firebase and Elasticsearch engine were used as back-end storage to aggregate data from various sources. Further, Node.js and Express Framework were used to develop several Representational State Transfer services useful for the visualization