Mobile Health

Mobile Health

Research Areas: Inference And Trust, X-informatics, Semantic eScience, Knowledge Provenance, Data Science, Web Science
Principal Investigator: Deborah L. McGuinness
Co Investigator:
Concepts: Artificial Intelligence, Web Science, Web Observatory, Ontology, Semantic eScience, Provenance, Linked Data, Machine Learning
Description:
Sensor-based health monitors are increasing in usage and are providing growing amounts of data about our health and daily activities. These monitors are increasingly included in or integrated with mobile devices. However, each sensor and/or device often has its own data format and interface, making it difficult for the data to be easily integrated with other sensors, or applications, or for algorithms to provide higher-level interpretations of data that may be more relevant to the user's health or lifestyle requirements, or for health providers to easily ingest and utilize for clinical purposes. We will gather requirements from health data and services providers to identify and refine use case requirements for representing health data in different systems. We will use semantic technologies to represent and reason with data available from a variety of sensors to yield an integrated and easily re-purposable view of a user’s health and activity state. We will develop of an ontology to model data streams available from mobile health sensors. We will then build a reference implementation of a semantically-integrated health data platform for mobile devices for the Android operating system. RPI is the only university to have obtained IBM’s Watson system and we will leverage Watson to connect the integrated health data view to relevant content. We will build a demonstration application to be distributed to our partners to assess requirement coverage and the usability of the system. Our initial use case will focus on weight management and fitness training, incorporating data from pedometers, heart monitors, and accelerometers however the platform will be designed to be easily extensible to other health sensor data and related focus areas.


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