Preprint Article Version 1 This version is not peer-reviewed

Mining Productive-associated Periodic Frequent Pattern in Body Sensor Data for Smart Home Care

Version 1 : Received: 18 April 2017 / Approved: 18 April 2017 / Online: 18 April 2017 (18:15:50 CEST)

A peer-reviewed article of this Preprint also exists.

Ismail, W.N.; Hassan, M.M. Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care. Sensors 2017, 17, 952. Ismail, W.N.; Hassan, M.M. Mining Productive-Associated Periodic-Frequent Patterns in Body Sensor Data for Smart Home Care. Sensors 2017, 17, 952.

Journal reference: Sensors 2017, 17, 952
DOI: 10.3390/s17050952

Abstract

The understanding of various health-oriented vital sign data generated from body sensor networks (BSN) and discovery of the association between the generated parameters is an important task that may assist and promote important decision making in healthcare. For example, in a smart home scenario where the occupants’ health status is continuously monitored remotely, it is essential to provide required assistance when an unusual or critical situation is detected in their vital sign data. In this paper, we present an efficient approach to mine the incomplete (partial) periodic patterns obtained from BSN data. In addition, we employ a correlation test on the generated patterns and introduce the productive-associated partial periodic frequent patterns as the set of correlated partial periodical frequent items. The combination of these measures has the advantage of empowering healthcare providers and patients for quality of diagnosis, and also for better treatment and smart care, especially for the elderly people at smart home. We developed an efficient algorithm named PPFP-Growth (Productive Periodic Frequent Pattern growth) to discover all productive associated partial periodic patterns using these measures. PPFP-Growth is efficient, and the productiveness measure removes uncorrelated periodic items. An experimental evaluation on synthetic and real datasets shows the efficiency of the proposed PPFP-Growth algorithm, and can filter a huge number of partial periodic patterns to reveal only the correlated ones.

Subject Areas

Body sensor network; Smart home, knowledge discovery in BSN data; frequent patterns; periodic patterns and productive pattern.

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