A robust indoor/outdoor detection method based on spatial and temporal features of sparse GPS measured positions

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

Research output: Contribution to journalArticle

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 letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions 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 clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.

Original languageEnglish
Pages (from-to)860-865
Number of pages6
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE102A
Issue number6
DOIs
Publication statusPublished - 2019 Jan 1

Fingerprint

Global positioning system
Classifiers
Random Forest
Learning systems
Classify
Classifier
Positioning
Experiments
Machine Learning
Calculate
Unknown
Cell
Demonstrate
Experiment

Keywords

  • Indoor/outdoor detection
  • Random forest classifier
  • Sparse GPS

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

@article{f7a47a37004a4365b5b9cb84cfe3a148,
title = "A robust indoor/outdoor detection method based on spatial and temporal features of sparse GPS measured positions",
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 letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions 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 clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.",
keywords = "Indoor/outdoor detection, Random forest classifier, Sparse GPS",
author = "Sae Iwata and Kazuaki Ishikawa and Toshinori Takayama and Masao Yanagisawa and Nozomu Togawa",
year = "2019",
month = "1",
day = "1",
doi = "10.1587/transfun.E102.A.860",
language = "English",
volume = "E102A",
pages = "860--865",
journal = "IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences",
issn = "0916-8508",
publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",
number = "6",

}

TY - JOUR

T1 - A robust indoor/outdoor detection method based on spatial and temporal features of sparse GPS measured positions

AU - Iwata, Sae

AU - Ishikawa, Kazuaki

AU - Takayama, Toshinori

AU - Yanagisawa, Masao

AU - Togawa, Nozomu

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions 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 clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.

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 letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions 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 clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.

KW - Indoor/outdoor detection

KW - Random forest classifier

KW - Sparse GPS

UR - http://www.scopus.com/inward/record.url?scp=85069560046&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85069560046&partnerID=8YFLogxK

U2 - 10.1587/transfun.E102.A.860

DO - 10.1587/transfun.E102.A.860

M3 - Article

AN - SCOPUS:85069560046

VL - E102A

SP - 860

EP - 865

JO - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

JF - IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

SN - 0916-8508

IS - 6

ER -