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

    2 引用 (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.

    元の言語English
    ホスト出版物のタイトル2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
    出版者IEEE Computer Society
    2018-September
    ISBN(電子版)9781538660959
    DOI
    出版物ステータス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

    Other

    Other8th IEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018
    Germany
    Berlin
    期間18/9/218/9/5

    Fingerprint

    Global positioning system
    Classifiers
    Learning systems
    Experiments

    ASJC Scopus subject areas

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

    これを引用

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

    研究成果: Conference contribution

    Iwata, S, Ishikawa, K, Takayama, T, Yanagisawa, M & Togawa, N 2018, Robust indoor/outdoor detection method based on sparse GPS positioning information. : 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018. 巻. 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. : 2018 IEEE 8th International Conference on Consumer Electronics - Berlin, ICCE-Berlin 2018. 巻 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. 巻 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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    AU - Ishikawa, Kazuaki

    AU - Takayama, Toshinori

    AU - Yanagisawa, Masao

    AU - Togawa, Nozomu

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    AB - 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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