A Factor Graph Based Indoor Localization Approach for Healthcare

Shiyu Zheng; Ziheng Zhou; Qi Zhang; Shanshan Zhang; Ao Peng; Lingxiang Zheng; Huiru Zheng; Haiying Wang

doi:10.1109/BIBM58861.2023.10385673

A Factor Graph Based Indoor Localization Approach for Healthcare

Shiyu Zheng, Ziheng Zhou, Qi Zhang, Shanshan Zhang, Ao Peng, Lingxiang Zheng, Huiru Zheng, Haiying Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Abstract

In healthcare facilities, indoor localization technology has a broad range of applications. Traditional Pedestrian Dead Reckoning (PDR) and WiFi fingerprint-based methods each have their limitations. To address these challenges, this study introduces a multi-source fusion indoor localization system that uses a Factor Graph to integrate inertial positioning algorithms with WiFi fingerprint-based localization. The system processes accelerometer and gyroscope data using a data-driven PDR algorithm. For WiFi localization, considering that the extensive data collection required is a significant barrier to the deployment of WiFi-based localization methods, the proposed approach applies Gaussian process regression techniques to limited WiFi fingerprint data, significantly reducing initial deployment costs and enhancing accuracy. Finally, the entire system employs a Factor Graph for the integration of the data-driven PDR and WiFi fingerprint localization results. Experimental results show that, compared to using only inertial or WiFi data for localization, this method significantly improves localization accuracy. The findings suggest that this approach could prompt the utilization of indoor localization technology in healthcare facilities.

Original language	English
Title of host publication	2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Editors	Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
Publisher	IEEE
Pages	3257-3264
Number of pages	8
ISBN (Electronic)	979-8-3503-3748-8
ISBN (Print)	979-8-3503-3749-5
DOIs	https://doi.org/10.1109/BIBM58861.2023.10385673
Publication status	Published (in print/issue) - 18 Jan 2024

Publication series

Name
Publisher	IEEE Control Society
ISSN (Print)	2156-1125
ISSN (Electronic)	2156-1133

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Factor Graph
fingerprint positioning
Gaussian Process Regression
Indoor Positioning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BIBM58861.2023.10385673

A_Factor_Graph_Based_Indoor_Localization_Approach_for_Healthcare-camera-readyAuthor Accepted Manuscript, 888 KB

Cite this

Zheng, S., Zhou, Z., Zhang, Q., Zhang, S., Peng, A., Zheng, L., Zheng, H., & Wang, H. (2024). A Factor Graph Based Indoor Localization Approach for Healthcare. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 3257-3264). IEEE. https://doi.org/10.1109/BIBM58861.2023.10385673

@inproceedings{5d0aaac09747474db6e44110d69211bb,

title = "A Factor Graph Based Indoor Localization Approach for Healthcare",

abstract = "In healthcare facilities, indoor localization technology has a broad range of applications. Traditional Pedestrian Dead Reckoning (PDR) and WiFi fingerprint-based methods each have their limitations. To address these challenges, this study introduces a multi-source fusion indoor localization system that uses a Factor Graph to integrate inertial positioning algorithms with WiFi fingerprint-based localization. The system processes accelerometer and gyroscope data using a data-driven PDR algorithm. For WiFi localization, considering that the extensive data collection required is a significant barrier to the deployment of WiFi-based localization methods, the proposed approach applies Gaussian process regression techniques to limited WiFi fingerprint data, significantly reducing initial deployment costs and enhancing accuracy. Finally, the entire system employs a Factor Graph for the integration of the data-driven PDR and WiFi fingerprint localization results. Experimental results show that, compared to using only inertial or WiFi data for localization, this method significantly improves localization accuracy. The findings suggest that this approach could prompt the utilization of indoor localization technology in healthcare facilities.",

keywords = "Factor Graph, fingerprint positioning, Gaussian Process Regression, Indoor Positioning",

author = "Shiyu Zheng and Ziheng Zhou and Qi Zhang and Shanshan Zhang and Ao Peng and Lingxiang Zheng and Huiru Zheng and Haiying Wang",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.",

year = "2024",

month = jan,

day = "18",

doi = "10.1109/BIBM58861.2023.10385673",

language = "English",

isbn = "979-8-3503-3749-5",

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editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",

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Zheng, S, Zhou, Z, Zhang, Q, Zhang, S, Peng, A, Zheng, L, Zheng, H & Wang, H 2024, A Factor Graph Based Indoor Localization Approach for Healthcare. in X Jiang, H Wang, R Alhajj, X Hu, F Engel, M Mahmud, N Pisanti, X Cui & H Song (eds), 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, pp. 3257-3264. https://doi.org/10.1109/BIBM58861.2023.10385673

A Factor Graph Based Indoor Localization Approach for Healthcare. / Zheng, Shiyu; Zhou, Ziheng; Zhang, Qi et al.
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). ed. / Xingpeng Jiang; Haiying Wang; Reda Alhajj; Xiaohua Hu; Felix Engel; Mufti Mahmud; Nadia Pisanti; Xuefeng Cui; Hong Song. IEEE, 2024. p. 3257-3264.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Zheng, Shiyu

AU - Zhou, Ziheng

AU - Zhang, Qi

AU - Zhang, Shanshan

AU - Peng, Ao

AU - Zheng, Lingxiang

AU - Zheng, Huiru

AU - Wang, Haiying

PY - 2024/1/18

Y1 - 2024/1/18

N2 - In healthcare facilities, indoor localization technology has a broad range of applications. Traditional Pedestrian Dead Reckoning (PDR) and WiFi fingerprint-based methods each have their limitations. To address these challenges, this study introduces a multi-source fusion indoor localization system that uses a Factor Graph to integrate inertial positioning algorithms with WiFi fingerprint-based localization. The system processes accelerometer and gyroscope data using a data-driven PDR algorithm. For WiFi localization, considering that the extensive data collection required is a significant barrier to the deployment of WiFi-based localization methods, the proposed approach applies Gaussian process regression techniques to limited WiFi fingerprint data, significantly reducing initial deployment costs and enhancing accuracy. Finally, the entire system employs a Factor Graph for the integration of the data-driven PDR and WiFi fingerprint localization results. Experimental results show that, compared to using only inertial or WiFi data for localization, this method significantly improves localization accuracy. The findings suggest that this approach could prompt the utilization of indoor localization technology in healthcare facilities.

AB - In healthcare facilities, indoor localization technology has a broad range of applications. Traditional Pedestrian Dead Reckoning (PDR) and WiFi fingerprint-based methods each have their limitations. To address these challenges, this study introduces a multi-source fusion indoor localization system that uses a Factor Graph to integrate inertial positioning algorithms with WiFi fingerprint-based localization. The system processes accelerometer and gyroscope data using a data-driven PDR algorithm. For WiFi localization, considering that the extensive data collection required is a significant barrier to the deployment of WiFi-based localization methods, the proposed approach applies Gaussian process regression techniques to limited WiFi fingerprint data, significantly reducing initial deployment costs and enhancing accuracy. Finally, the entire system employs a Factor Graph for the integration of the data-driven PDR and WiFi fingerprint localization results. Experimental results show that, compared to using only inertial or WiFi data for localization, this method significantly improves localization accuracy. The findings suggest that this approach could prompt the utilization of indoor localization technology in healthcare facilities.

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KW - Gaussian Process Regression

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SN - 979-8-3503-3749-5

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EP - 3264

BT - 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

A2 - Jiang, Xingpeng

A2 - Wang, Haiying

A2 - Alhajj, Reda

A2 - Hu, Xiaohua

A2 - Engel, Felix

A2 - Mahmud, Mufti

A2 - Pisanti, Nadia

A2 - Cui, Xuefeng

A2 - Song, Hong

PB - IEEE

ER -

A Factor Graph Based Indoor Localization Approach for Healthcare

Abstract

Publication series

Bibliographical note

Keywords

UN SDGs

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