Haptic data compression for rehabilitation databases

A rehabilitation database is a concept that utilizes the quantitative data acquired from rehabilitation robots. By applying database techniques to rehabilitation robot data, many applications will become possible. As one example, this paper discusses how to match rehabilitation data based on a dynamic programming method. It is to be anticipated that large amounts of data and long calculation times for searching will be two serious issues for rehabilitation databases. Therefore, this paper proposes a method based on two techniques: feature extraction and nonlinear quantization. Both techniques have the combined features of data compression and good recognition performance. Hence, the matching of compressed data has a high recognition rate, even if the compression ratio is very high. The performance of the proposed method is evaluated through experimental data of 500 trials.

[1]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[2]  Kiyoshi Ohishi,et al.  Bilateral control using compressor/decompressor under the low-rate communication network , 2009, 2009 IEEE International Conference on Mechatronics.

[3]  Toshiaki Tsuji,et al.  A Robot Measuring Upper Limb Range of Motion for Rehabilitation Database , 2013, J. Robotics Mechatronics.

[4]  Toshiaki Tsuji,et al.  Command Recognition of Robot with Low Dimension Whole-Body Haptic Sensor , 2010 .

[5]  Hermano Igo Krebs,et al.  Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy , 2003, Auton. Robots.

[6]  Toshiaki Tsuji,et al.  Whole-Body Force Sensation by Force Sensor With Shell-Shaped End-Effector , 2009, IEEE Transactions on Industrial Electronics.

[7]  Toshiaki Tsuji,et al.  Real-time personal identification based on haptic information , 2011, 2011 IEEE International Conference on Robotics and Automation.

[8]  D. Reinkensmeyer,et al.  Review of control strategies for robotic movement training after neurologic injury , 2009, Journal of NeuroEngineering and Rehabilitation.

[9]  W. Rymer,et al.  Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study , 2006, Journal of NeuroEngineering and Rehabilitation.

[10]  N. Hogan,et al.  A novel approach to stroke rehabilitation , 2000, Neurology.

[11]  Toshiaki Tsuji,et al.  Stiffness control of a pneumatic rehabilitation robot for exercise therapy with multiple stages , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  K. Ohnishi,et al.  Haptic data compression/decompression using DCT for motion copy system , 2009, 2009 IEEE International Conference on Mechatronics.

[13]  Antonio Ortega,et al.  A comparison of different haptic compression techniques , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[14]  Sandra Hirche,et al.  Perception-Based Data Reduction and Transmission of Haptic Data in Telepresence and Teleaction Systems , 2008, IEEE Transactions on Signal Processing.

[15]  Toshiro Noritsugu,et al.  Application of rubber artificial muscle manipulator as a rehabilitation robot , 1996, Proceedings 5th IEEE International Workshop on Robot and Human Communication. RO-MAN'96 TSUKUBA.

[16]  Robert Riener,et al.  ARMin: a robot for patient-cooperative arm therapy , 2007, Medical & Biological Engineering & Computing.

[17]  S. Micera,et al.  Robotic techniques for upper limb evaluation and rehabilitation of stroke patients , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.