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

    1 Citation (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
    Volume2018-September
    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

    Other

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

    Fingerprint

    Global positioning system
    Classifiers
    Learning systems
    Experiments

    Keywords

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

    ASJC Scopus subject areas

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

    Cite this

    Iwata, S., Ishikawa, K., Takayama, T., Yanagisawa, M., & Togawa, N. (2018). Robust indoor/outdoor detection method based on sparse GPS positioning information. In 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018 (Vol. 2018-September). [8576188] IEEE Computer Society. https://doi.org/10.1109/ICCE-Berlin.2018.8576188

    Robust indoor/outdoor detection method based on sparse GPS positioning information. / Iwata, Sae; Ishikawa, Kazuaki; Takayama, Toshinori; Yanagisawa, Masao; Togawa, Nozomu.

    2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018. Vol. 2018-September IEEE Computer Society, 2018. 8576188.

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

    Iwata, S, Ishikawa, K, Takayama, T, Yanagisawa, M & Togawa, N 2018, Robust indoor/outdoor detection method based on sparse GPS positioning information. in 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018. vol. 2018-September, 8576188, IEEE Computer Society, 8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018, Berlin, Germany, 18/9/2. https://doi.org/10.1109/ICCE-Berlin.2018.8576188
    Iwata S, Ishikawa K, Takayama T, Yanagisawa M, Togawa N. Robust indoor/outdoor detection method based on sparse GPS positioning information. In 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018. Vol. 2018-September. IEEE Computer Society. 2018. 8576188 https://doi.org/10.1109/ICCE-Berlin.2018.8576188
    Iwata, Sae ; Ishikawa, Kazuaki ; Takayama, Toshinori ; Yanagisawa, Masao ; Togawa, Nozomu. / Robust indoor/outdoor detection method based on sparse GPS positioning information. 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018. Vol. 2018-September IEEE Computer Society, 2018.
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    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.",
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