Using periodicity intensity to detect long term behaviour change

Feiyan Hu, Alan F. Smeaton, Eamonn Newman, Matthew Buman

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

4 Scopus citations

Abstract

This paper introduces a new way to analyse and visualize quantified-self or lifelog data captured from any lifelogging device over an extended period of time. The mechanism works on the raw, unstructured lifelog data by detecting periodicities, those repeating patters that occur within our lifestyles at different frequencies including daily, weekly, seasonal, etc. Focusing on the 24 hour cycle, we calculate the strength of the 24-hour periodicity at 24-hour intervals over an extended period of a lifelog. Changes in this strength of the 24-hour cycle can illustrate changes or shifts in underlying human behavior. We have performed this analysis on several lifelog datasets of durations from several weeks to almost a decade, from recordings of training distances to sleep data. In this paper we use 24 hour accelerometer data to illustrate the technique, showing how changes in human behavior can be identified.

Original languageEnglish (US)
Title of host publicationUbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers
PublisherAssociation for Computing Machinery, Inc
Pages1069-1074
Number of pages6
ISBN (Electronic)9781450335751
DOIs
StatePublished - Sep 7 2015
EventACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015 - Osaka, Japan
Duration: Sep 7 2015Sep 11 2015

Publication series

NameUbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers

Other

OtherACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015
CountryJapan
CityOsaka
Period9/7/159/11/15

Keywords

  • Detecting behaviour change
  • Lifelogging
  • Periodicity
  • Periodogram intensities

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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  • Cite this

    Hu, F., Smeaton, A. F., Newman, E., & Buman, M. (2015). Using periodicity intensity to detect long term behaviour change. In UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers (pp. 1069-1074). (UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers). Association for Computing Machinery, Inc. https://doi.org/10.1145/2800835.2800962