a new ontology-based approach for human activity recognition from gps data

Mobile technologies have deployed a variety of internet-based services via location-based services. the adoption of these services by users has led to mammoth amounts of trajectory data. to use these services effectively, the analysis of this kind of data across different application domains is required in order to identify the activities that users might need to do in different places. researchers from different communities have developed models and techniques to extract activity types from such data but they have mainly focused on the geometric properties of trajectories, and do not consider the semantic aspect of moving objects. the current work proposes a new ontology-based approach so as to recognize human activity from gps data for understanding and interpreting mobility data. the performance of the approach was tested and evaluated using a dataset acquired by a user over a year within the urban area in the city of calgary in 2010. it was observed that the accuracy of the results obtained was related to the availability of the points of interest around the places that the user had stopped. moreover, an evaluation experiment was carried out, which revealed the effectiveness of the proposed method with an improvement of 50% performance with complexity trend of an o(n).