Abstract
As a result of the rapid development of smartphone-based indoor localization technology, location-based services in indoor spaces have become a topic of interest. However, to date, the rich data resulting from indoor localization and navigation applications have not been fully exploited, which is significant for trajectory correction and advanced indoor map information extraction. In this paper, an integrated location acquisition method utilizing activity recognition and semantic information extraction is proposed for indoor mobile localization. The location acquisition method combines pedestrian dead reckoning (PDR), human activity recognition (HAR) and landmarks to acquire accurate indoor localization information. Considering the problem of initial position determination, a hidden Markov model (HMM) is utilized to infer the user’s initial position. To provide an improved service for further applications, the landmarks are further assigned semantic descriptions by detecting the user’s activities. The experiments conducted in this study confirm that a high degree of accuracy for a user’s indoor location can be obtained. Furthermore, the semantic information of a user’s trajectories can be extracted, which is extremely useful for further research into indoor location applications.
Highlights
Location-based services (LBSs) have been popular for many years
With the wide availability of smartphones, a large amount of research has been conducted in recent years targeting indoor localization
In order to evaluate the performance of the human activity recognition (HAR) classification, 10-fold cross-validation [39] was used
Summary
Global navigation satellite systems (GNSSs) can provide good localization services outdoors, there is still no dominant indoor positioning technique [1]. Most of the existing indoor localization technologies require additional infrastructure, such as ultra-wideband [2], laser scanning systems (LSSs), radiofrequency identification (RFID) [3] and Wi-Fi access points [4]. These approaches often require extensive labor and time. To solve this problem, pedestrian dead reckoning (PDR) has recently been proposed as one of the most promising technologies for indoor localization [5]. PDR suffers from error accumulation when the travel time is long
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