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Open AccessArticle

Mobile User Indoor-Outdoor Detection through Physical Daily Activities

1
MOE Key Laboratory for Intelligent and Network Security, Xi’an Jiaotong University, Xi’an 710049, China
2
Division of Operation and Maintenance Engineering, Luleå University of Technology, 97187 Luleå, Sweden
3
Department of Electrical Engineering, Raja University, 34148 Qazvin, Iran
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(3), 511; https://doi.org/10.3390/s19030511
Received: 11 December 2018 / Revised: 22 January 2019 / Accepted: 23 January 2019 / Published: 26 January 2019
(This article belongs to the Collection Wearable and Unobtrusive Monitoring Systems)
An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications. View Full-Text
Keywords: sensor-based indoor-outdoor detection; location-based services; human daily activity; smartphone motion sensors; machine learning; context awareness sensor-based indoor-outdoor detection; location-based services; human daily activity; smartphone motion sensors; machine learning; context awareness
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Esmaeili Kelishomi, A.; Garmabaki, A.; Bahaghighat, M.; Dong, J. Mobile User Indoor-Outdoor Detection through Physical Daily Activities. Sensors 2019, 19, 511.

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