Exploring Variability in IoT Data for Human Activity Recognition

Yuiko Sakuma, Sofia Kleisarchaki, Levent Gurgen, Hiroaki Nishi

研究成果: Conference contribution

抄録

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%.

本文言語English
ホスト出版物のタイトルProceedings
ホスト出版物のサブタイトルIECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society
出版社IEEE Computer Society
ページ5312-5318
ページ数7
ISBN(電子版)9781728148786
DOI
出版ステータスPublished - 2019 10
イベント45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 - Lisbon, Portugal
継続期間: 2019 10 142019 10 17

出版物シリーズ

名前IECON Proceedings (Industrial Electronics Conference)
2019-October

Conference

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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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