The Similarity Quantification of Multidimensional Time Series Data Sets in Surveillance Applications for Health Monitoring System

Abstract

Abstract:In the last decade, Data mining techniques have been applied for sensor data in a wide range of applications. Like health care monitoring systems, manufacturing process. Intruder detection, database management and other. A lot of data mining engineering is based on the calculation of the similarity between two models of sensor data. A number of representations and Equality measures for multi -assign time series was suggested in the literature. In this paper, we describe a new way of calculating whether two similarities in the series of multiple series are based on the temporal version of Smith-Waterman (SW), a known information algorithm. Next, we apply our method to detect data on the demand for care of the elderly to early detection of the disease. Our procedure absorber is difficulties linked to the data uncertainty and aggregation that often occurs during treatment sensor data. The trials will take place one aging-in-place installation, Tiger Place placed in Columbia, MO. To validate our method we used data on nine no-portable a sensor for one-p located in TigerPlace apartments, combined with information of one Electronic Health Record (EHR).We deliver a set of experiments studying the temporal version of SW properties, with experiences on TigerPlace dataset.