Accuracy evaluations of human moving pattern using communication quality based on machine learning

Wataru Kawakami, Kenji Kanai, Bo Wei, Jiro Katto

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

4 Citations (Scopus)

Abstract

In this paper, we performed human moving pattern recognition using communication quality: cellular download throughputs, Received Signal Strength Indicators (RSSIs) and cellular base station IDs. We apply three machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) and evaluate recognition accuracy of human moving patterns. Results conclude that the communication quality can recognize moving patterns with high accuracy.

Original languageEnglish
Title of host publication2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-2
Number of pages2
Volume2017-January
ISBN (Electronic)9781509040452
DOIs
Publication statusPublished - 2017 Dec 19
Event6th IEEE Global Conference on Consumer Electronics, GCCE 2017 - Nagoya, Japan
Duration: 2017 Oct 242017 Oct 27

Other

Other6th IEEE Global Conference on Consumer Electronics, GCCE 2017
CountryJapan
CityNagoya
Period17/10/2417/10/27

Keywords

  • Communication quality
  • Human activity recognition
  • Machine learning
  • Mobile sensing

ASJC Scopus subject areas

  • Media Technology
  • Instrumentation
  • Electrical and Electronic Engineering

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  • Cite this

    Kawakami, W., Kanai, K., Wei, B., & Katto, J. (2017). Accuracy evaluations of human moving pattern using communication quality based on machine learning. In 2017 IEEE 6th Global Conference on Consumer Electronics, GCCE 2017 (Vol. 2017-January, pp. 1-2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE.2017.8229351