A Multidimensional Time-Series Similarity Measure With Applications to Eldercare Monitoring

作者：Hajihashemi Zahra^*; Popescu Mihail^*

来源：IEEE Journal of Biomedical and Health Informatics, 2016, 20(3): 953-962.

DOI：10.1109/JBHI.2015.2424711

• Genetic algorithm
• multiattribute time series
• Smith-Waterman (SW) algorithm
• time-series data mining

In the last decade, data mining techniques have been applied to sensor data in a wide range of application domains, such as healthcare monitoring systems, manufacturing processes, intrusion detection, database management, and others. Many data mining techniques are based on computing the similarity between two sensor data patterns. A variety of representations and similarity measures for multiattribute time series have been proposed in the literature. In this paper, we describe a novelmethod for computing the similarity of two multiattribute time series based on a temporal version of Smith-Waterman (SW), a well-known bioinformatics algorithm. We then apply ourmethod to sensor data froman eldercare application for early illness detection. Our method mitigates difficulties related to data uncertainty and aggregation that often arise when processing sensor data. The experiments take place at an aging-in-place facility, TigerPlace, located in Columbia, MO, USA. To validate our method, we used data from nonwearable sensor networks placed in TigerPlace apartments, combined with information from an electronic health record. We provide a set of experiments that investigate temporal version of SWproperties, together with experiments on TigerPlace datasets. On a pilot sensor dataset from nine residents, with a total of 1902 days and around 2.1 million sensor hits of collected data, we obtained an average abnormal events prediction F-measure of 0.75.

• 出版日期2016-5