Exploring Variability in IoT Data for Human Activity Recognition

Yuiko Sakuma, Sofia Kleisarchaki, Levent Gurgen, Hiroaki Nishi

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

Abstract

Human Activity Recognition (HAR) is a well-studied scientific area that has gained much traction with the rise of Internet of Things (IoT). Despite the interest in HAR for a wide spectrum of domains (technological, medical, etc.) only a few works exist, which study the variability in IoT data. To correctly perceive this variability, it is essential to dynamically model the evolving context of daily-life activities. Additionally, it is required to reduce the calculation cost of HAR, which is crucial for security and real-time applications. For the purpose of dynamically modeling, three context-aware approaches are formalized along with a context-free baseline. This study demonstrates improvements in terms of both of accuracy and calculation cost by considering variability in IoT data; our experimental study on real datasets reduced calculation cost by 20% while increasing accuracy by 20%.

Original languageEnglish
Title of host publicationProceedings
Subtitle of host publicationIECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE Computer Society
Pages5312-5318
Number of pages7
ISBN (Electronic)9781728148786
DOIs
Publication statusPublished - 2019 Oct
Event45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
Duration: 2019 Oct 142019 Oct 17

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
Volume2019-October

Conference

Conference45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019
CountryPortugal
CityLisbon
Period19/10/1419/10/17

Keywords

  • Human Activity Recognition
  • Internet of Things
  • Spatio-Temporal Context
  • Variability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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  • Cite this

    Sakuma, Y., Kleisarchaki, S., Gurgen, L., & Nishi, H. (2019). Exploring Variability in IoT Data for Human Activity Recognition. In Proceedings: IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society (pp. 5312-5318). [8927472] (IECON Proceedings (Industrial Electronics Conference); Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/IECON.2019.8927472