Applying Hierarchical Information with Learning Approach for Activity Recognition

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• 作者：Hoai-Viet To (1) viet.to@jaist.ac.jp
  Hoai-Bac Le (23) lhbac@fit.hcmus.edu.vn
  Mitsuru Ikeda (1) ikeda@jaist.ac.jp
• 刊名：Lecture Notes in Computer Science
• 出版年：2011
• 出版时间：2011
• 年：2011
• 卷：7027
• 期：1
• 页码：231-242
• 全文大小：224.1 KB
• 参考文献：1. Ainsworth, B.E., Haskell, W.L., Whitt, M.C., Irwin, M.L., Swartz, A.M., Strath, S.J., O’brien, W.L., Bassett, D.R., Schmitz, K.H., Emplaincourt, P.O., Jacobs, D.R., Leon, A.S.: Compendium of physical activities: an update of activity codes and met intensities. Medicine and Science in Sports & Exercise 32, 498–504 (2000)
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• 作者单位：1. School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan2. Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam3. Faculty of Information Technology, Vietnamese National University, Ho Chi Minh City, Vietnam
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

This paper discusses the problem of applying ontology for activity recognition and proposes a hierarchical classification approach by using categorize information among activities with machine learning method. In activity recognition problem, machine learning approaches have the ability to adapt to various real environments but actual setting do not often obtain enough quality data to construct a good model for recognizing multiple activities. Our approach exploits the hierarchical structure of activities to overcome the problem uncertainty and incomplete data for multi-class classification in real home setting datasets. While slightly improves the overall recognition accuracy from 59% to 63%, hierarchical approach can recognize infrequent activities such as “Going out to work” and “Taking medication” with accuracies of 80% and 56% respectively. Those activities had recognition accuracies lower than random guess in previous learning method. The preliminary results support the idea to develop a methodology to utilize semantic information represented in ontologies for activity recognition problem.