Change point detection using multivariate exponentially weighted moving average (MEWMA) for optimal parameter in online activity monitoring

Naveed Khan*, Sally McClean, Shuai Zhang, Chris Nugent

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, wearable sensors are integrating frequently and rapidly into our daily life day by day. Such smart sensors have attracted a lot of interest due to their small sizes and reasonable computational power. For example, body worn sensors are widely used to monitor daily life activities and identify meaningful events. Hence, the capability to detect, adapt and respond to change performs a key role in various domains. A change in activities is signaled by a change in the data distribution within a time window. This change marks the start of a transition from an ongoing activity to a new one. In this paper, we evaluate the proposed algorithm’s scalability on identifying multiple changes in different user activities from real sensor data collected from various subjects. The Genetic algorithm (GA) is used to identify the optimal parameter set for Multivariate Exponentially Weighted Moving Average (MEWMA) approach to detect change points in sensor data. Results have been evaluated using a real dataset of 8 different activities for five different users with a high accuracy from 99.2 % to 99.95 % and G-means from 67.26 % to 83.20 %.

Original languageEnglish
Title of host publicationUbiquitous Computing and Ambient Intelligence - 10th International Conference, UCAmI 2016, Proceedings
EditorsPino Caballero-Gil, Carmelo R. García, Alexis Quesada-Arencibia, Mike Burmester
PublisherSpringer Verlag
Pages156-165
Number of pages10
ISBN (Print)9783319487458
DOIs
Publication statusPublished - 02 Nov 2016
Externally publishedYes
Event10th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2016 - San Bartolomde Tirajana, Gran Canaria, Spain
Duration: 29 Nov 201602 Dec 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10069 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2016
CountrySpain
CitySan Bartolomde Tirajana, Gran Canaria
Period29/11/201602/12/2016

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Accelerometer
  • Activity monitoring
  • Genetic algorithm
  • Multiple change points

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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