Study on adaptive Kalman filtering algorithms in human movement tracking

During the rehabilitation process, the movements of the post-stroke patients need to be localized and learned so that incorrect movements can be instantly identified and modified. This is vital and necessary for patients to recover and improve their mobility toward normal life. This paper presents an adaptive Kalman filter algorithm for position estimation of patients' movements. In order to improve the performance of dynamic performance of the filter, a modified adaptive filtering algorithm is investigated. The feasibility and efficiency of the proposed adaptive algorithm is verified by simulation results.

[1]  Jake K. Aggarwal,et al.  Human Motion Analysis: A Review , 1999, Comput. Vis. Image Underst..

[2]  E. Granum,et al.  Skin colour detection under changing lighting conditions , 1999 .

[3]  Huosheng Hu,et al.  CSM-420 A Survey - Human Movement Tracking and Stroke Rehabilitation , 2004 .

[4]  Osama Masoud,et al.  Image-based reconstruction for view-independent human motion recognition , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[5]  Ronald Azuma,et al.  Predictive tracking for augmented reality , 1995 .

[6]  Namgyu Kim,et al.  POSTRACK: a low cost real-time motion tracking system for VR application , 2001, Proceedings Seventh International Conference on Virtual Systems and Multimedia.

[7]  Thomas B. Moeslund,et al.  Multiple cues used in model-based human motion capture , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[8]  David J. Fleet,et al.  Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.

[9]  Yoshiaki Shirai,et al.  Hand posture estimation by combining 2-D appearance-based and 3-D model-based approaches , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[10]  Mohan M. Trivedi,et al.  Human Body Model Acquisition and Tracking Using Voxel Data , 2003, International Journal of Computer Vision.

[11]  Greg Welch,et al.  Motion Tracking: No Silver Bullet, but a Respectable Arsenal , 2002, IEEE Computer Graphics and Applications.

[12]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[13]  Hendrik Johannes Luinge,et al.  Inertial sensing of human movement , 2002 .

[14]  Eric Foxlin,et al.  Motion Tracking Requirements and Technologies , 2002 .

[15]  Daniel Thalmann,et al.  A real time anatomical converter for human motion capture , 1996 .

[16]  Rama Chellappa,et al.  Estimating the Kinematics and Structure of a Rigid Object from a Sequence of Monocular Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Cristian Sminchisescu,et al.  Covariance scaled sampling for monocular 3D body tracking , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[18]  Huosheng Hu,et al.  Building A Visual Tracking System for Home-Based Rehabilitation , 2003 .