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