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

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

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
出版社IEEE Computer Society
ISBN(電子版)9781538660959
DOI
出版ステータスPublished - 2018 12 13
イベント8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 - Berlin, Germany
継続期間: 2018 9 22018 9 5

出版物シリーズ

名前IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
2018-September
ISSN(印刷版)2166-6814
ISSN(電子版)2166-6822

Other

Other8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
国/地域Germany
CityBerlin
Period18/9/218/9/5

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 産業および生産工学
  • メディア記述

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