Understanding how trace segmentation impacts transportation mode detection

Yung-Ju Chang, Mark W. Newman

研究成果: Conference contribution同行評審

摘要

Transportation mode (TM) detection is one of the activity recognition tasks in ubiquitous computing. A number of previous studies have compared the performance of various classifiers for TM detection. However, the current study is the first work aiming to understand how TM detection performance is impacted by how the recorded location traces are segmented into data segments for training a classifier. In our preliminary experiments we examine three trace segmentation (TS) methods-Uniform Duration (UniDur), Uniform Number of Location Points (UniNP), and Uniform Distance (UniDis)-and compare their performance on detecting different transportation modes. The results indicate that while driving can be more accurately detected by using UniDis method, walking and bus can be more accurately detected by using UniDur method. This suggests that choosing a right TS method for training a TM classifier is an important step to accurately detect particular transportation modes.

原文English
主出版物標題UbiComp'12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing
頁面625-626
頁數2
DOIs
出版狀態Published - 1 十二月 2012
事件14th International Conference on Ubiquitous Computing, UbiComp 2012 - Pittsburgh, PA, United States
持續時間: 5 九月 20128 九月 2012

出版系列

名字UbiComp'12 - Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Conference

Conference14th International Conference on Ubiquitous Computing, UbiComp 2012
國家United States
城市Pittsburgh, PA
期間5/09/128/09/12

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