Spatial Information Extraction using Hidden Correlations

Chun Chih Lo, Kuo Hsuan Hsu, Mong Fong Homg, Yau-Hwang Kuo

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

Abstract

To promote independent living and elderly care in a smart living space, monitoring the activities of daily living and detecting upcoming critical situations are crucial. Thus, exploiting the spatial aspect related to the movement of the resident and their relations to a specific region is important. However, most of existing solutions do not consider related spatial aspect or it is incorporated in a very limited way, in which most solutions need to know the deployment location of sensors. To achieve this, a very time consuming sensor labeling process is needed. In this paper, an unsupervised spatial information extraction technique for smart living space is developed. This technique establishes an abstract spatial representation called virtual location for a physical living space without knowing the actual deployment location of the sensors in that space. A virtual location is achieved by utilizing our extended Louvain modularity method to extract key spatial features from the hidden correlations in the observations perceived through the sensors. By considering the characteristics of different sensors, the effectiveness of virtual location can be further improved. Two real world datasets are used to validate the applicability of the propose technique, and the experimental results for them achieve 90.3% and 71.7% V-measure values respectively.

Original languageEnglish
Title of host publication2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538660096
DOIs
Publication statusPublished - 2018 Dec 18
Event29th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2018 - Bologna, Italy
Duration: 2018 Sep 92018 Sep 12

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
Volume2018-September

Other

Other29th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2018
CountryItaly
CityBologna
Period18-09-0918-09-12

Fingerprint

Sensors
Labeling
Monitoring

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Lo, C. C., Hsu, K. H., Homg, M. F., & Kuo, Y-H. (2018). Spatial Information Extraction using Hidden Correlations. In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2018 [8580676] (IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC; Vol. 2018-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PIMRC.2018.8580676
Lo, Chun Chih ; Hsu, Kuo Hsuan ; Homg, Mong Fong ; Kuo, Yau-Hwang. / Spatial Information Extraction using Hidden Correlations. 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC).
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abstract = "To promote independent living and elderly care in a smart living space, monitoring the activities of daily living and detecting upcoming critical situations are crucial. Thus, exploiting the spatial aspect related to the movement of the resident and their relations to a specific region is important. However, most of existing solutions do not consider related spatial aspect or it is incorporated in a very limited way, in which most solutions need to know the deployment location of sensors. To achieve this, a very time consuming sensor labeling process is needed. In this paper, an unsupervised spatial information extraction technique for smart living space is developed. This technique establishes an abstract spatial representation called virtual location for a physical living space without knowing the actual deployment location of the sensors in that space. A virtual location is achieved by utilizing our extended Louvain modularity method to extract key spatial features from the hidden correlations in the observations perceived through the sensors. By considering the characteristics of different sensors, the effectiveness of virtual location can be further improved. Two real world datasets are used to validate the applicability of the propose technique, and the experimental results for them achieve 90.3{\%} and 71.7{\%} V-measure values respectively.",
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Lo, CC, Hsu, KH, Homg, MF & Kuo, Y-H 2018, Spatial Information Extraction using Hidden Correlations. in 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2018., 8580676, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC, vol. 2018-September, Institute of Electrical and Electronics Engineers Inc., 29th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2018, Bologna, Italy, 18-09-09. https://doi.org/10.1109/PIMRC.2018.8580676

Spatial Information Extraction using Hidden Correlations. / Lo, Chun Chih; Hsu, Kuo Hsuan; Homg, Mong Fong; Kuo, Yau-Hwang.

2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8580676 (IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC; Vol. 2018-September).

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

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Lo CC, Hsu KH, Homg MF, Kuo Y-H. Spatial Information Extraction using Hidden Correlations. In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8580676. (IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC). https://doi.org/10.1109/PIMRC.2018.8580676