Abstract

Internet of Things (IoT) has enabled several applications related to data analytics. In this paper, an intuitive method for optimizing activity detection data is presented. Further applications include exploring detection accuracies of physical activities such as walking intensity and movement on stairs. This method utilizes different Microcontroller Units (MCUs) with embedded sensors which are used for activity detection. Additionally, this method also incorporates supervised learning - more specifically the Fine Gaussian SVM, to generate a predictive model for activity optimization.

Original languageEnglish (US)
Title of host publication2018 9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538637319
DOIs
StatePublished - Feb 1 2019
Event9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018 - Zakynthos, Greece
Duration: Jul 23 2018Jul 25 2018

Publication series

Name2018 9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018

Conference

Conference9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018
CountryGreece
CityZakynthos
Period7/23/187/25/18

ASJC Scopus subject areas

  • Artificial Intelligence
  • Hardware and Architecture
  • Information Systems
  • Software
  • Safety, Risk, Reliability and Quality
  • Social Sciences (miscellaneous)

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  • Cite this

    Khondoker, F., Thornton, T., Spanias, A., & Shanthamallu, U. S. (2019). Optimizing activity detection via sensor fusion. In 2018 9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018 [8633623] (2018 9th International Conference on Information, Intelligence, Systems and Applications, IISA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IISA.2018.8633623