%0 Conference Paper %1 6816334 %A Huang, Wenhao %A Li, Man %A Hu, Weisong %A Song, Guojie %A Xing, Xingxing %A Xie, Kunqing %B Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on %D 2013 %K bachelor:2015:elger gps sensors %P 962-966 %R 10.1109/FSKD.2013.6816334 %T Cost sensitive GPS-based activity recognition %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6816334&newsearch=true&queryText=activity%20recognition%20gps %X GPS-based activity recognition is extremely important for high-level analysis and location based services. Trajectories of people are highly imbalanced from spatial and temporal perspectives. Many existing researches achieve good results on recognizing activities with lots of GPS logs, such as working and staying at home. However, these approaches usually fail at activities with few trajectory records. In this paper, we propose a cost sensitive GPS-based activity recognition model to improve accuracy of minority activities which could imply users' personal preferences. The approach aims at providing more balanced results. We first propose a cost function to measure spatial and temporal regularities of each activity on a stay point. Then we incorporate cost function into activity recognition algorithm. We take hidden Markov model as an example in this study. Experiments show good performance of our approach in several evaluation metrics. It could provide more balanced and valuable activity recognition results from GPS trajectories.

@inproceedings{6816334, abstract = {GPS-based activity recognition is extremely important for high-level analysis and location based services. Trajectories of people are highly imbalanced from spatial and temporal perspectives. Many existing researches achieve good results on recognizing activities with lots of GPS logs, such as working and staying at home. However, these approaches usually fail at activities with few trajectory records. In this paper, we propose a cost sensitive GPS-based activity recognition model to improve accuracy of minority activities which could imply users' personal preferences. The approach aims at providing more balanced results. We first propose a cost function to measure spatial and temporal regularities of each activity on a stay point. Then we incorporate cost function into activity recognition algorithm. We take hidden Markov model as an example in this study. Experiments show good performance of our approach in several evaluation metrics. It could provide more balanced and valuable activity recognition results from GPS trajectories.}, added-at = {2015-10-12T23:53:26.000+0200}, author = {Huang, Wenhao and Li, Man and Hu, Weisong and Song, Guojie and Xing, Xingxing and Xie, Kunqing}, biburl = {https://www.bibsonomy.org/bibtex/2b1af3c7c6c1801b4fd87bb078df790cf/rev4ge}, booktitle = {Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on}, description = {IEEE Xplore Abstract - Cost sensitive GPS-based activity recognition}, doi = {10.1109/FSKD.2013.6816334}, interhash = {8a94dcfa3bfb06c2fdd5b16b3d863205}, intrahash = {b1af3c7c6c1801b4fd87bb078df790cf}, keywords = {bachelor:2015:elger gps sensors}, month = {July}, pages = {962-966}, timestamp = {2015-10-12T23:53:26.000+0200}, title = {Cost sensitive GPS-based activity recognition}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6816334&newsearch=true&queryText=activity%20recognition%20gps}, year = 2013 }