Semantic sleep management service in healthcare sensor networks

In this paper, we propose a semantic sleep management service using healthcare sensors(blood pressure, blood sugar, body temperature, snoring, sleep apnea) and private health information(age, gender, weight, smoking amount and drinking quantity). The proposed service finds the best private sleep pattern by acquiring sensor observations, providing analysis results of private sleep trend by analyzing data gathered from a heterogeneous individual healthcare sensors and private health information in order to enhance sleep quality of each individual. To this end, we use newly-made sleep management sensor to detect the snoring time and the number of sleep apnea and use semantic web technologies to represent standard specification and processing of sensor networks using ontologies that allow representation of structural properties of event types and constraints between them.