Using Genetic Algorithms for Optimal Change Point Detection in Activity Monitoring

Naveed Khan, Sally McClean, Shuai Zhang, Chris Nugent

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

1 Citation (Scopus)

Abstract

Activity Monitoring is a key feature of health and well-being assessment that has received increased consideration from the research community over the last few decades. Body worn sensors and smart devices are widely used in Activity Monitoring in order to capture and classify large amounts of data over short periods of time, in a relatively un-obtrusive manner. Change point detection is a technique at the core of the data processing of the sensory data recorded used to identify the transition from one underlying time series generation model to another. The sudden change in mean, variance or both may represent change point in time series data. Accurate and automatic change point detection in data is not only used to identify events (transition from one activity to another), however, can also be used for labelling activities to generate real world annotated datasets. This paper proposes a genetic algorithm (GA) that identifies the optimal set of parameters for a Multivariate Exponentially Weighted Moving Average (MEWMA) approach to change point detection. The proposed technique optimizes different parameters of the MEWMA in an effort to find the maximum F-measure, which subsequently identifies the exact location of the change point from an existing activity to a new one. Results have been evaluated based on real and synthetic datasets collected from accelerometer data during a set of 8 different activities for two users with a high degree of accuracy form 99.4% to 99.8% and F-measure to 66.7%.

Original languageEnglish
Title of host publicationProceedings - IEEE 29th International Symposium on Computer-Based Medical Systems, CBMS 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages318-323
Number of pages6
ISBN (Electronic)9781467390361
DOIs
Publication statusPublished - 18 Aug 2016
Externally publishedYes
Event29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016 - Belfast, United Kingdom
Duration: 20 Jun 201623 Jun 2016

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2016-August
ISSN (Print)1063-7125

Conference

Conference29th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2016
CountryUnited Kingdom
CityBelfast
Period20/06/201623/06/2016

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

  • Accelerometer
  • Activity monitoring
  • Change point detection
  • Genetic Algorithm

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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