HARMONI: Context-aware filtering of sensor data for continuous remote health monitoring

Abstract

A promising architecture for remote healthcare monitoring involves the use of a pervasive device (such as a cellular phone), which aggregates data from multiple body-worn medical sensors and transmits the data to the backend. Unfortunately, the volume of data generated by increasingly sophisticated continuouslyactive sensors can overwhelm the resources on the mobile device. We propose imbuing the mobile device with the intelligence to perform context-aware filtering of sensor data streams in order to reduce transmissions in cases where the observed data corresponds to the norm expected by the system in a given context. To investigate the efficacy of this technique, we implemented the HARMONI middleware on a mobile device, and used it to collect real sensor data from users. Our experiments demonstrate that context-aware filtering can reduce the uplink bandwidth requirements of the system by up to 72%. © 2008 IEEE.