期刊名称：Advanced Computing : an International Journal
印刷版ISSN：2229-726X
电子版ISSN：2229-6727
出版年度：2012
卷号：3
期号：4
DOI：10.5121/acij.2012.3408
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a significant opportunity for improving the quality of health care services. Physiological signals like ECG, EEG, SpO2, BP etc. can be monitor through wireless sensor networks and analyzed with the help of data mining techniques. These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health care decisions online. Because of the high speed and huge volume data set in data streams, the traditional classification technologies are no longer applicable. The most important criteria are to solve the real-time data streams mining problem with ‘concept drift’ efficiently. This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting concept drift in data stream, then gives a significant summary of existing approaches to the problem of concept drift. The work is focused on applying these real time stream mining algorithms on vital signals of human body in Wireless Body Area Network( WBAN) based health care environment.
关键词：Wireless Sensor Networks; WBAN; Real-time data stream mining; concept-drift; physiological signal ;processing; Health Care
