Abstract
Our ultimate goal is to develop autonomous mobile home healthcare robots which closely monitor and evaluate the patients’ motor function, and their at-home training therapy process, providing automatically calling for medical personnel in emergency situations. In our previous study, we developed basic algorithms for tracking, measuring, and behavior recognition of human subjects by a mobile robot, thus, demonstrated the feasibility of the idea of bio-monitoring home healthcare mobile robots. In this study, in order to realize effective bio-monitoring robots, we investigated 1) color based particle filter subject tracking with proposed depth likelihood integration to control the weights of particles; 2) control schemes for acquiring stable image sources for further human motion analysis, especially, the algorithms for reducing the camera vibration due to the acceleration and deceleration of the robot; 3) human activity recognition using contour data of the tracked human subjects extracted from depth images. Results showed that, depending on depth data can be quite useful as an observation by simplifying state space in 2D rather than 3D state space, and, a fuzzy control algorithm could decrease the vibration due to the acceleration and deceleration. Finally, the human activity recognition could be achieved with a high correct rate, by using geometric parameters extracted from contour data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Nergui, M., Yoshida, Y., Gonzalez, J., Koike, Y., Sekine, M., Yu, W.: Human Motion Tracking and Measurement by a Mobile Robot. In: Intelligent Robotics and Applications, pp. P21–P30 (2012)
Kwon, H., Yoon, Y., Park, J.B., et al.: Person tracking with a mobile robot using two uncalibrated independently moving cameras. In: IEEE International Conference on Robotics and Automation, Barcelona, Spain, pp. 2877–2883 (2005)
Gupta, M., Behera, L., et al.: A Novel Approach of Human Motion Tracking with the Mobile Robotic Platform. In: International Conference on Modelling and Simulation, UKSim (2011)
Cunha, J., Pedrosa, E., et al.: Using a Depth Camera for Indoor Robot Localization and Navigation. In: Robotics Science and Systems (RSS) Conference on Campus at the University of Southern California, Poster Presentation, p. P5 (2011)
Biswas, J., Veloso, M.: Depth Camera based Localization and Navigation for Indoor Mobile Robots (2011), http://www.cs.cmu.edu/~mmv/papers/11rssw-KinectLocalization.pdf
Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEEÂ 77(2) (February 1989)
Wang, L., Tan, T., et al.: Silhouette Analysis-Based Gait Recognition for Human Identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12) (December 2003)
Uddin, M.Z., Kim, T.S., et al.: Video-based Human Gait Recognition Using Depth Imaging and Hidden Markov Model: A Smart System for Smart Home. In: SHB2010 - 3rd International Symposium on Sustainable Healthy Buildings, Seoul, Korea (May 27, 2010)
Rohila, N., et al.: Abnormal Gait Recognition. International Journal on Computer Science and Engineering 2(5), 1544–1551 (2010)
Pushpa Rani, M., et al.: An efficient gait recognition system for human identification using modified ICA. International Journal of Computer Science & Information Technology 2(1) (Feburary 2010)
Nummiaro, K., Koller-Meier, E., Van Gool, L.: Object Tracking with an Adaptive Color-Based Particle Filter. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 353–360. Springer, Heidelberg (2002)
GarcĂa, G.M., Klein, D.A., StĂĽckler, J., Frintrop, S., Cremers, A.B.: Adaptive Multi-cue 3D Tracking of Arbitrary Objects. In: Pinz, A., Pock, T., Bischof, H., Leberl, F. (eds.) DAGM and OAGM 2012. LNCS, vol. 7476, pp. 357–366. Springer, Heidelberg (2012)
Nergui, M., Yoshida, Y., Imamoglu, N., Gonzalez, J., Otake, M., Yu, W.: Human Body Contour Data Based Activity Recognition. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2013 (accepted, 2013)
Nergui, M., Yoshida, Y., Imamoglu, N., Gonzalez, J., Otake, M., Yu, W.: Human Activity Recognition Using Body Contour Parameters Extracted from Depth Images. Journal of Medical Imaging and Health Informatics (accepted)
Paris, S.: Particle Filter Color Tracker. In: Mathworks File Exchange (2011), http://www.mathworks.com/matlabcentral/fileexchange/17960-particle-filtSer-color-tracker
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Imamoglu, N., Nergui, M., Yoshida, Y., Gonzalez, J., Yu, W. (2013). Control Strategies and Particle Filter for RGB-D Based Human Subject Tracking and Behavior Recognition by a Bio-monitoring Mobile Robot. In: Lee, J., Lee, M.C., Liu, H., Ryu, JH. (eds) Intelligent Robotics and Applications. ICIRA 2013. Lecture Notes in Computer Science(), vol 8102. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40852-6_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-40852-6_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40851-9
Online ISBN: 978-3-642-40852-6
eBook Packages: Computer ScienceComputer Science (R0)