Activity Recognition and Semantic Description for Indoor Mobile Localization.

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.