Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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Torque Estimation of the Human Elbow Joint using the MVS (Muscle Volume Sensor)

근 부피 센서를 이용한 인체 팔꿈치 관절의 동작 토크 추정

  • Lee, Hee Don (Department of Mechanical Engineering, Hanyang Univ.) ;
  • Lim, Dong Hwan (Department of Mechanical Engineering, Hanyang Univ.) ;
  • Kim, Wan Soo (Department of Mechanical Engineering, Hanyang Univ.) ;
  • Han, Jung Soo (Department of Mechanical System Engineering, Hansung Univ.) ;
  • Han, Chang Soo (Department of Mechanical Engineering, Hanyang Univ.) ;
  • An, Jae Yong (Department of Pathology and Orthopedics, Cheil General Hospital, Kwandong Univ.)
  • Received : 2013.01.21
  • Accepted : 2013.03.12
  • Published : 2013.06.01
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Abstract

This study uses a muscle activation sensor and elbow joint model to develop an estimation algorithm for human elbow joint torque for use in a human-robot interface. A modular-type MVS (Muscle Volume Sensor) and calibration algorithm are developed to measure the muscle activation signal, which is represented through the normalization of the calibrated signal of the MVS. A Hill-type model is applied to the muscle activation signal and the kinematic model of the muscle can be used to estimate the joint torques. Experiments were performed to evaluate the performance of the proposed algorithm by isotonic contraction motion using the KIN-COM$^{(R)}$ equipment at 5, 10, and 15Nm. The algorithm and its feasibility for use as a human-robot interface are verified by comparing the joint load condition and the torque estimated by the algorithm.

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References

  1. Sato, T., Nishida, Y., Ichikawa, J., Hatamura, Y., and Mizoguchi, H., "Active understanding of human intention by a robot through monitoring of human behavior," Proc. Of the IEEE/RSJ/GI International Conference on Intelligent Robots and systems, Vol. 1, pp. 405-414, 1994.
  2. Han, H., Kwon, S., and Kim, J., "Optical muscle activation sensor for bionic applications," Journal of the Korean Society for Precision Engineering, Vol. 26, No. 7, pp. 15-21, 2009.
  3. Fleischer, C. and Hommel, G., "A human-exoskeleton interface utilizing electromyography," IEEE Transactions on Robotics, Vol. 24, No. 4, pp. 872-882, 2008. https://doi.org/10.1109/TRO.2008.926860
  4. Seo, A., Jang, H., Kim, W., Han, C., and Han, J., "Development and verification of a volume sensor for measuring human behavior," International Journal of Precision Engineering and Manufacturing, Vol. 13, No. 6, pp. 899-904, 2012. https://doi.org/10.1007/s12541-012-0117-0
  5. Orizio, C., Diemont, B., Esposito, F., Alfonsi, E., Parrinello, G., Moglia, A., and Veicsteinas, A., "Surface mechanomyogram reflects the changes in the mechanical properties of muscle at fatigue," European Journal of Applied Physiology and Occupational Physiology, Vol. 80, No. 4, pp. 276-284, 1999. https://doi.org/10.1007/s004210050593
  6. Moromugi, S., Koujinal, Y., Ariki, S., Okamoto, A., Tanaka, T., Feng, M. Q., and Ishimatsu, T., "Muscle stiffness sensor to control an assistance device for the disabled," Artificial Life and Robotics, Vol. 8, No. 1, pp. 42-45, 2004. https://doi.org/10.1007/s10015-004-0286-8
  7. Epstein, M. and Herzog, W., "Theoretical models of skeletal muscle: biological and mathematical considerations," Wiley, pp. 26-63, 1998.
  8. Cavallaro, E. E., Rosen, J., Perry, J. C., and Burns, S., "Real-time myoprocessors for a neural controlled powered exoskeleton arm," IEEE Transations on Biomedical Engineering, Vol. 53, No. 11, pp. 2387-2395, 2006. https://doi.org/10.1109/TBME.2006.880883
  9. Venture, G., Yamane, K., and Nakamura, Y., "Identifying musculo-tendon parameters of human body based on the musculo-skeletal dynamics computation and Hill-Stroeve muscle model," Proceedings of 2005 5th IEEE-RAS International Conference on Humanoid Robots, Tsukuba, Japan, pp. 351-356, 2005.
  10. Nam, Y. S., "Estimation of Muscle-tendon Model Parameters Based on Numeric Optimization," Journal of the Korean Society for Precision Engineering, Vol. 26, No. 6, pp. 122-130, 2009.
  11. Garner, B. A. and Pandy, M. G., "Estimation of musculotendon properties in the human upper limb," Annals of Biomedical Engineering, Vol. 31, No. 2, pp. 207-220, 2003. https://doi.org/10.1114/1.1540105
  12. Holzbaur, K. R. S., Murray, W. M., and Delp, S. L., "A model of the upper extremity for simulating musculoskeletal surgery and analyzing neuromuscular control," Annals of Biomedical Engineering, Vol. 33, No. 6, pp. 829-840, 2005. https://doi.org/10.1007/s10439-005-3320-7
  13. Pigeon, P., Yahia, L. H., and Feldman, A. G., "Moment arms and lengths of human upper limb muscles as functions of joint angles," Journal of biomechanics, Vol. 29, No. 10, pp. 1365-1370, 1996. https://doi.org/10.1016/0021-9290(96)00031-0
  14. Buchanan, T. S., Lloyd, D. G., Manal, K., and Besier, T. F., "Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command," Journal of applied biomechanics, vol. 20, No. 4, pp. 367-395, 2004.