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The Scientific World Journal
Volume 2014, Article ID 280207, 22 pages
Research Article

Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking

1Graduate School of Engineering, Chiba University, Chiba 263-8522, Japan
2Research Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
3Department of Electronic Technology, University of Seville, 41012 Seville, Spain
4Institute of Robotics and Intelligent Information Processing, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
5Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
6Spanish National Research Council, Bioengineering Group, 28500 Madrid, Spain

Received 20 August 2014; Accepted 4 November 2014; Published 21 December 2014

Academic Editor: Ji-Xiang Du

Copyright © 2014 Nevrez Imamoglu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals. Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy.