Monitoring of Medication Intake Using a Camera System

This paper presents a computer vision system for monitoring medication intake in the context of home care services. We use a method based on color and shape to detect the body parts and the medication bottles. Color is used for skin detection, and the shape is used to distinguish the face from the hands and to differentiate bottles of medicine. To track these objects, we use a method based on color histograms, Hu moments, and edges. For the recognition of medication intake, we use a Petri network and event recognition. Our method has an accuracy of more than 75% and allows the detection of the medication intake in various scenarios where the user is cooperative.

[1]  Peter F. Sturm,et al.  Adaptive Tracking of Non-Rigid Objects Based on Color Histograms and Automatic Parameter Selection , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[2]  Sung-Hyuk Cha,et al.  On measuring the distance between histograms , 2002, Pattern Recognit..

[3]  Mubarak Shah,et al.  A computer vision system for monitoring medication intake , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

[4]  Jacques Verly,et al.  The State of the Art in Multiple Object Tracking Under Occlusion in Video Sequences , 2003 .

[5]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[6]  Marc Parizeau,et al.  Experiments on eigenfaces robustness , 2002, Object recognition supported by user interaction for service robots.

[7]  Guillaume-Alexandre Bilodeau,et al.  Face and Hands Detection and Tracking Applied to the Monitoring of Medication Intake , 2008, 2008 Canadian Conference on Computer and Robot Vision.

[8]  Yong Rui,et al.  Better proposal distributions: object tracking using unscented particle filter , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[9]  Ramakant Nevatia,et al.  Video-based event recognition: activity representation and probabilistic recognition methods , 2004, Comput. Vis. Image Underst..

[10]  Nikolaos G. Bourbakis,et al.  A survey of skin-color modeling and detection methods , 2007, Pattern Recognit..

[11]  Linda G. Shapiro,et al.  Computer Vision , 2001 .

[12]  James L. Peterson,et al.  Petri Nets , 1977, CSUR.

[13]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[14]  Sergio A. Velastin,et al.  People tracking in surveillance applications , 2006, Image Vis. Comput..

[15]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Larry S. Davis,et al.  Representation and Recognition of Events in Surveillance Video Using Petri Nets , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[17]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  J. Meunier,et al.  Video Surveillance of Medication Intake , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Oswald Lanz,et al.  Approximate Bayesian multibody tracking , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Cheng-Chew Lim,et al.  Hand and face segmentation using motion and color cues in digital image sequences , 2001, IEEE International Conference on Multimedia and Expo, 2001. ICME 2001..

[21]  Ioannis Pitas,et al.  Face localization and facial feature extraction based on shape and color information , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[22]  Hélène Laurent,et al.  Comparative study of global invariant descriptors for object recognition , 2008, J. Electronic Imaging.

[23]  MeerPeter,et al.  Kernel-Based Object Tracking , 2003 .