Integration of positioning and activity context information for lifelog in urban city area

Smartphone-based Lifelog (automatically annotating the users' daily experience from multisensory streams on smartphones) is in great need. Accurate positioning under any situation is one of the most significant techniques for a desirable Lifelog. This paper proposes to detect location-related activities and use the activity information to improve positioning accuracy. In the proposed system, a human activity recognition module is developed to extract location-related activities from multisensory streams of smartphones. After that, the proposed system integrates activity information with PDR-based positioning results in a context-based map-matching framework. The developed system can be used for both outdoor and indoor scenarios. Moreover, the developed indoor positioning method is used to determine the positions of calibration points automatically in an auto-calibration Wi-Fi positioning system. The proposed methods achieve 3.1-m accuracy in outdoor and average 2.2-m accuracy in indoor situations.