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Sensors 2016, 16(11), 1784; doi:10.3390/s16111784

Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms

1
School of Computing and Information Engineering, Ulster University, Coleraine, Co., Londonderry BTT52 1SA, UK
2
School of Computing and Mathematics, Ulster University, Jordanstown, Co., Antrim BT37 0QB, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Vladimir Villarreal and Carmelo García
Received: 6 May 2016 / Revised: 17 October 2016 / Accepted: 18 October 2016 / Published: 26 October 2016
(This article belongs to the Special Issue Selected Papers from UCAmI, IWAAL and AmIHEALTH 2015)
View Full-Text   |   Download PDF [1902 KB, uploaded 26 October 2016]   |  

Abstract

In recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient’s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%. View Full-Text
Keywords: multivariate change detection; activity monitoring; multivariate exponentially weighted moving average; accelerometer; genetic algorithm; change-point detection multivariate change detection; activity monitoring; multivariate exponentially weighted moving average; accelerometer; genetic algorithm; change-point detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Khan, N.; McClean, S.; Zhang, S.; Nugent, C. Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms. Sensors 2016, 16, 1784.

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