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Mobile User Indoor-Outdoor Detection Through Physical Daily Activities
Xi'an Jiaotong University, Xi'an, China.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.ORCID-id: 0000-0003-2976-5229
Raja University, Qazvin, Iran.
Xi'an Jiaotong University, Xi'an, China.
2019 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, nr 3, artikkel-id 511Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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.

sted, utgiver, år, opplag, sider
MDPI, 2019. nr 3, artikkel-id 511
Emneord [en]
sensor-based indoor-outdoor detection, location-based services, human daily activity, smartphone motion sensors, machine learning, context awareness
HSV kategori
Forskningsprogram
Drift och underhållsteknik
Identifikatorer
URN: urn:nbn:se:ltu:diva-72799DOI: 10.3390/s19030511ISI: 000459941200074Scopus ID: 2-s2.0-85060629071OAI: oai:DiVA.org:ltu-72799DiVA, id: diva2:1286105
Merknad

Validerad;2019;Nivå 2;2019-02-06 (svasva)

Tilgjengelig fra: 2019-02-06 Laget: 2019-02-06 Sist oppdatert: 2019-04-11bibliografisk kontrollert

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