Seamless Pedestrian Navigation Methodology Optimized for Indoor/Outdoor Detection

Zeng, Qinghua; Wang, Jingxian; Meng, Qian; Zhang, Xiaoxue; Zeng, Shijie

doi:10.1109/jsen.2017.2764509

Cited by 75 publications

(25 citation statements)

References 25 publications

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“…Motion recognition on smartphones is of great significance, and plays an important role in indoor and outdoor positioning [ 1 ], human activity recognition [ 2 ], remote health monitoring [ 3 ] and location-based services [ 4 ]. For indoor positioning, motion recognition is the basis of the pedestrian dead reckoning algorithm, which needs to estimate a user’s step length by identifying the motion state of the user and the attitude of the smartphone [ 5 , 6 , 7 ]. Equipped with a variety of sensors, smartphones are more attractive for mobile computing and awareness with ubiquitous and user-friendly superior features when compared to other devices.…”

Section: Introductionmentioning

confidence: 99%

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer

Liu

Wang

et al. 2018

Sensors

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Accelerometers, gyroscopes and magnetometers in smartphones are often used to recognize human motions. Since it is difficult to distinguish between vertical motions and horizontal motions in the data provided by these built-in sensors, the vertical motion recognition accuracy is relatively low. The emergence of a built-in barometer in smartphones improves the accuracy of motion recognition in the vertical direction. However, there is a lack of quantitative analysis and modelling of the barometer signals, which is the basis of barometer’s application to motion recognition, and a problem of imbalanced data also exists. This work focuses on using the barometers inside smartphones for vertical motion recognition in multi-floor buildings through modelling and feature extraction of pressure signals. A novel double-windows pressure feature extraction method, which adopts two sliding time windows of different length, is proposed to balance recognition accuracy and response time. Then, a random forest classifier correlation rule is further designed to weaken the impact of imbalanced data on recognition accuracy. The results demonstrate that the recognition accuracy can reach 95.05% when pressure features and the improved random forest classifier are adopted. Specifically, the recognition accuracy of the stair and elevator motions is significantly improved with enhanced response time. The proposed approach proves effective and accurate, providing a robust strategy for increasing accuracy of vertical motions.

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Section: Introductionmentioning

confidence: 99%

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer

Liu

Wang

et al. 2018

Sensors

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“…In another example, Zeng et al [26] proposed a solution for indoor/outdoor positioning systems. Their approach integrates PDR and light and magnetic sensor signals for indoor positioning.…”

Section: Related Workmentioning

confidence: 99%

A Smartphone Indoor Positioning System Using Hybrid Localization Technology

Gang

Pyun

2019

Energies

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As smartphone built-in sensors, wireless technologies, and processor computing power become more advanced and global positioning system (GPS)-based positioning technologies are improving, location-based services (LBS) have become a part of our daily lives. At the same time, demand has grown for LBS applications in indoor environments, such as indoor path finding and navigation, marketing, entertainment, and location-based information retrieval. In this paper, we demonstrate the design and implementation of a smartphone-based indoor LBS system for location services consisting of smartphone applications and a server. The proposed indoor LBS system uses hybrid indoor positioning methods based on Bluetooth beacons, Geomagnetic field, Inertial Measurement Unit (IMU) sensors, and smartphone cameras and can be used for three types of indoor LBS applications. The performance of each positioning method demonstrates that our system retains the desired accuracy under experimental conditions. As these results illustrate that our system can maintain positioning accuracy to within 2 m 80% of the time, we believe our system can be a real solution for various indoor positioning service needs.

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“…However, positioning technologies and algorithms are too coupled in the solution and the DR algorithm used is too dependent on an initial good location estimation. In [34], a PDR system with handover support between indoor and outdoor operational modes is presented. The indoor or outdoor location is detected using the light and magnetic reads obtained from the mobile device sensors.…”

Section: Hybrid Positioningmentioning

confidence: 99%

Offloading Positioning onto Network Edge

Santa

Fernández

Sánchez-Iborra

et al. 2018

Wireless Communications and Mobile Computing

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While satellite or cellular positioning implies dedicated hardware or network infrastructure functions, indoor navigation or novel IoT positioning techniques include flexible storage and computation requirements that can be fulfilled by both end-devices or cloud back-ends. Hybrid positioning systems support the integration of several algorithms and technologies; however, the common trend of delegating position calculation and storage of local geoinformation to mobile devices or centralized servers causes performance degradation in terms of delay, battery usage, and waste of network resources. The strategy followed in this work is offloading this computation effort onto the network edge, following a Mobile Edge Computing (MEC) approach. MEC nodes in the access network of the mobile device are in charge of receiving navigation data coming from both the smart infrastructure and mobile devices, in order to compute the final position following a hybrid approach. With the aim of supporting mobility and the access to multiple networks, an Information Centric Networking (ICN) solution is used to access generic position information resources. The presented system currently supports WiFi, Bluetooth LE, GPS, cellular and NFC technologies, involving both indoor and outdoor positioning, using fingerprinting and proximity for indoor navigation, and the integration of smart infrastructure data sources such as the door opening system within real smart campus deployment. Evaluations carried out reveal latency improvements of 50%, as compared with a regular configuration where position fixes are computed by mobile devices; at the same time the MEC solution offers extra flexibility features to manage positioning databases and algorithms and move extensive computation from constrained devices to the edge.

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Seamless Pedestrian Navigation Methodology Optimized for Indoor/Outdoor Detection

Cited by 75 publications

References 25 publications

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer

Double-Windows-Based Motion Recognition in Multi-Floor Buildings Assisted by a Built-In Barometer

A Smartphone Indoor Positioning System Using Hybrid Localization Technology

Offloading Positioning onto Network Edge

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