Robust indoor/outdoor detection method based on sparse GPS positioning information

Sae Iwata, Kazuaki Ishikawa, Toshinori Takayama, Masao Yanagisawa, Nozomu Togawa

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

3 Citations (Scopus)

Abstract

Cell phones with GPS function as well as GPS loggers are widely used and we can easily obtain users' geographic information. Now classifying the measured GPS positions into indoor/outdoor positions is one of the major challenges. In this paper, we propose a robust indoor/outdoor detection method based on sparse GPS positioning information utilizing machine learning. Given a set of clusters of measured positions whose center position shows the user's estimated stayed position, we calculate the feature values composed of: Positioning accuracy, spatial features and temporal feature of measured positions included in every cluster. Then a random forest classifier learns these feature values of the known data set. Finally, we classify the unknown sequence of measured positions into indoor/outdoor positions using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the $F-{1}$ measure of 0.9836, which classifies measured positions into indoor/outdoor ones with almost no errors.

Original languageEnglish
Title of host publication2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
PublisherIEEE Computer Society
ISBN (Electronic)9781538660959
DOIs
Publication statusPublished - 2018 Dec 13
Event8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 - Berlin, Germany
Duration: 2018 Sep 22018 Sep 5

Publication series

NameIEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
Volume2018-September
ISSN (Print)2166-6814
ISSN (Electronic)2166-6822

Other

Other8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
Country/TerritoryGermany
CityBerlin
Period18/9/218/9/5

Keywords

  • Indoor/Outdoor Detection
  • Random Forest Classifier
  • Sparse GPS

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Media Technology

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