Context-aware and personalized event filtering for low-overhead continuous remote health monitoring

Abstract

A particularly compelling vision of long-term remote health monitoring advocates the use of a personal pervasive device (such as a cellphone) as an intermediate relay, which transports data streams from multiple body-worn sensors to a backend analytics infrastructure. Unfortunately, a pure relay-based functionality on the cellphone is inadequate in the longer term, as increasingly sophisticated medical sensors impose unnacceptably high uplink traffic and energy consumption costs on the mobile device. To address this challenge, we are building an event-processing middleware, called HARMONI, which enables the pervasive device to perform context-aware processing and event filtering on the sensor data streams and locally extract higher-level features of interest, thereby reducing the volume of transmitted data. This paper presents the design and architectural components of HARMONI, with special emphasis on its implementation of context-aware event processing. This paper then demonstrates that the mobile device can extract localized context from the incoming sensor stream with sufficient accuracy to achieve satisfactory context-aware filtering. Our results also establish the need for personalizing such context extraction, as they show that similar sensor data patterns obtained from different individuals can imply significantly different activity contexts.