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JRM Vol.23 No.2 pp. 302-309
doi: 10.20965/jrm.2011.p0302
(2011)

Paper:

Response Evaluation of Rollover Recognition in Myoelectric Controlled Orthosis Using Pneumatic Rubber Muscle for Cancer Bone Metastasis Patient

Takeshi Ando*1,*2, Jun Okamoto*3, Mitsuru Takahashi*4,
and Masakatsu G. Fujie*1

*1Faculty of Science and Engineering, Waseda University, 59-309, 3-4-1 Ohkubo, Shinjuku-ku, Tokyo 169-8555, Japan

*2Dept. of Robotics & Design for Innovative Healthcare (Panasonic), Graduate School of Medicine, Osaka University, 1-7 Ymada-oka, Suita, Osaka 565-0871, Japan

*3Institute of Advanced Biomedical Engineering & Science, Tokyo Women’s Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666, Japan

*4Division of Orthopaedic Oncology, Shizuoka Cancer Center, Naga-izumi, Shizuoka 411-8777, Japan

Received:
September 30, 2010
Accepted:
February 16, 2011
Published:
April 20, 2011
Keywords:
myoelectric signal (EMG), response performance, pneumatic rubber muscle, rollover, orthosis
Abstract
The myoelectric controlled rollover support orthosis we have been developing for use in bone cancer metastasis requires high accuracy and quick response in signal processing to recognize movement. We quantitatively evaluated the response performance of recognizing rollover using our original Micro Macro Neural Network (MMNN) algorithm. Required response time was calculated as 60 ms by measuring contraction time for the muscle used in the orthosis to support rollover. TheMMNN recognized rollover 65 ms before it started. Rollover was recognized 5 ms after a myoelectric signal was generated, so the MMNN response was sufficient for the muscle to finish contraction before rollover started.
Cite this article as:
T. Ando, J. Okamoto, M. Takahashi, and M. Fujie, “Response Evaluation of Rollover Recognition in Myoelectric Controlled Orthosis Using Pneumatic Rubber Muscle for Cancer Bone Metastasis Patient,” J. Robot. Mechatron., Vol.23 No.2, pp. 302-309, 2011.
Data files:
References
  1. [1] T. Ando, J. Okamoto, and M. G. Fujie, “EMG Controlled Rollover Support System for Bone Cancer Metastasis Patient (1st report): EMG Signal Analysis in Rollover Movement as an Input Signal,” JSMBE, Vol.46, No.3, pp. 383-389, 2008.
  2. [2] T. Ando, J. Okamoto, and M. G. Fuji, “Micro Macro Neural Network to Recognize Rollover Movement,” Advanced Robotics, Vol.25, No.1-2, pp. 253-271, 2011.
  3. [3] T. Ando, J. Okamoto, M. Takahashi, and M. G. Fujie, “Intelligent Trunk Corset to Support Rollover of Cancer Bone Metastasis Patients,” IEEE/ASME Trans. on Mechatronics, Vol.15, No.2, pp. 181-190, 2010.
  4. [4] B. Hudgins, P. Parker, and R. Scott, “A New Strategy for Multifunction Myoelectric Control,” IEEE Trans. Biomed. Eng. Vol.40, No.1, pp. 82-94, 1993.
  5. [5] K. A. Farry, I. D.Walker, and R. G. Baraniuk, “Myoelectric Teleoperation of a Complex Robotic Hand,” IEEE Trans. on Robotics and Automation, Vol.12, No.5, pp. 775-787, 1996.
  6. [6] M. Khalil and J. Duchene, “Uterine EMG Analysis: a Dynamic Approach for Change Detection and Classification,” IEEE Trans. Biomed. Eng., Vol.47, No.6, pp. 748-756, 2000.
  7. [7] H. Yonghong, K. B. Englehart, B. Hudgins, and A. D. C. Chan, “A Gaussian Mixture Model Based Classification Scheme for Myoelectric Control of Powered Upper Limb Prostheses,” IEEE Trans. on Biomedical Engineering, Vol.52, No.11, pp. 1801-1811, 2005.
  8. [8] O. Fukuda, T. Tsuji, M. Kaneko, and A. Otsuka, “A Humanassisting Manipulator Teleoperated by EMG Signals and Arm Motions,” IEEE Trans. Robot. Autom., Vol.19, No.2, pp. 210-222, 2003.
  9. [9] M. Tsukamoto, T. Kondo, and K. Ito, “A Prosthetic Hand Control by Nonstationary EMG at the beginning of Motion,” IEICE technical report, MBE, Vol.105, No.577, pp. 41-44, 2006.
  10. [10] O. Fukuda, N. Bu, and T. Tsuji, “Control of an Externally Powered Prosthetic Forearm Using Raw-EMG Signals,” SICE Journal, Vol.40, No.11, pp. 1124-1131, 2004.
  11. [11] S. Noda, T. Tsuji, and M. Kaneko, “Pattern Discrimination of EMG Signals Using a New Recurrent Neural Network,” Proc. of Biomechanism Conf. Vol.22, pp. 167-170, 2001.
  12. [12] T. Noritsugu and T. Tanaka, “Application of Rubber Artificial Muscle Manipulator as a Rehabilitation Robot,” IEEE/ASME Trans. on Mechatronics, Vol.2, No.4, pp. 259-267, 1997.
  13. [13] S. Kawamura, Y. Hayakawa, M. Tamai, and T. Shimizu, “A Design of Motion-support Robots for Human Arms Using Hexahedron Rubber Actuators,” Proc. of the 1997 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Vol.3, pp. 1520-1526, 1997.
  14. [14] H. Kobayash, “Development of aMuscle Suit for Realizing AllMotion of the Upper Limb,” Proc. of 2004 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Vol.2, pp. 1630-1635, 2004.
  15. [15] K. Ueda, H. Fujimoto, and G. Shirogauchi, “Motion Assistance Apparatus and Method of Assisting Motion,” WO/2007/043308, April 19, 2007.
  16. [16] P. R. Cavanagh and P. V. Komi, “Electromechanical Delay in Human Skeletal Muscle under Concentric and Eccentric Contractions,” Euro. J. Applied Physi. and Occupational Physi, Vol.42, No.3, pp. 159-163, 1979.
  17. [17] E. J. Vos, J. Harlaar, and G. J. van Ingen Schenau, “Electromechanical Delay during Knee Extensor Contractions,” Medicine & Science in Sports & Exercise, Vol.23, No.10, pp. 1187-1193, 1991.
  18. [18] Y. Nishijima, T. Matui, K. Arita, and Y. Tomatu, “Definition of the Onset of Electromyographic Activity and Programming of Electro-Mechanical Delay Measurement: On Force Display in Voluntary Muscle Contraction,” Osaka Kyoiku University bulletin, Vol.44, No.1, pp. 105-113, 1995.
  19. [19] A. Waibel, T. Hanazawa, K. Hinton, K. Shikano, and K. Lang, “Phoneme Recognition Using Time-Delay Neural Networks,” IEEE Trans. on Acoustics, Speech, and Signal Processing, Vol.31, No.3, pp. 328-339, 1989.
  20. [20] J. Tani and S. Nolfi, “Learning to Perceive theWorld as Articulated: an Approach for Hierarchical Learning in Sensory-Motor Systems,” Neural Networks, Vol.12, pp. 1131-1141, 1999.
  21. [21] Z. Ling, L. Jiangli, L. Deyu,W. Tianfu, P. Yulan, and L. Yan, “Classification of Breast Tumors on Ultrasound Images Using a Hybrid Neural Network,” Proc. of The 1st Int. Conf. on Bioinformatics and Biomedical Engineering, pp. 574-576, Wuhan, China, 2007.
  22. [22] Y. Yamashita and J. Tani, “Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: a Humanoid Robot Experiment,” PLoS computational biology, Vol.4, No.11, pp. 1-18, 2008.
  23. [23] T. Ando, “Study on a myoelectric signal based accurate and quick rollover recognition in robotic orthosis for cancer bone metastasis patients,” Ph.D. thesis, Waseda University, pp. 114-124, 2011.
  24. [24] K. Bahman, “Design and Application of Neural Network,” Shokodo, Vol.43, 1996.

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