제어로봇시스템학회논문지 (Journal of Institute of Control, Robotics and Systems)

  • 제16권10호
  • /
  • Pages.927-932
  • /
  • 2010
  • /
  • 1976-5622(pISSN)
  • /
  • 2233-4335(eISSN)
제어로봇시스템학회 (Institute of Control, Robotics and Systems)
권호 표지
DOI QR코드

DOI QR Code

물리적 인간 기계 상호작용을 위한 근육의 기하학적 형상 변화를 이용한 상지부 움직임 예측

Prediction of the Upper Limb Motion Based on a Geometrical Muscle Changes for Physical Human Machine Interaction

  • Han, Hyon-Young (Korea Advanced Institute of Science and Technology) ;
  • Kim, Jung (Korea Advanced Institute of Science and Technology)
  • 투고 : 2010.06.10
  • 심사 : 2010.07.20
  • 발행 : 2010.10.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Estimation methods of motion intention from bio-signal present challenges in man machine interaction(MMI) to offer user's command to machine without control of any devices. Measurements of meaningful bio-signals that contain the motion intention and motion estimation methods from bio-signal are important issues for accurate and safe interaction. This paper proposes a novel motion estimation sensor based on a geometrical muscle changes, and a motion estimation method using the sensor. For estimation of the motion, we measure the circumference change of the muscle which is proportional to muscle activation level using a flexible piezoelectric cable (pMAS, piezo muscle activation sensor), designed in band type. The pMAS measures variations of the cable band that originate from circumference changes of muscle bundles. Moreover, we estimate the elbow motion by applying the sensor to upper limb with least square method. The proposed sensor and prediction method are simple to use so that they can be used to motion prediction device and methods in rehabilitation and sports fields.

키워드

참고문헌

  1. 이희돈, 유승남, 이승훈, 장재호, 한정수, 한창수, "상지 근육지원용 웨어러블 로봇을 위한 명령 신호 생성 기법 개발," 제어.로봇.시스템학회 논문지, 제15권 제2호, pp. 176-183, 2009. https://doi.org/10.5302/J.ICROS.2009.15.2.176
  2. Z. Z. Bien, K. H. Park, J. W. Jung, and J. H. Do, "Intention reading is essential in human-friendly interfaces for the elderly and the handicapped," Proc.of IEEE Transactions on Industrial Electronics, Daejeon, Korea, vol. 52, pp. 1500-1505, Dec. 2005. https://doi.org/10.1109/TIE.2005.858734
  3. R. Song and K. Y. Tong, "Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations," Medical and Biological Engineering and Computing, vol. 43, pp. 473-480, 2005. https://doi.org/10.1007/BF02344728
  4. Y. Koike and M. Kawato, "Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model," Biological Cybernetics, vol. 73, pp. 291-300, 1995. https://doi.org/10.1007/BF00199465
  5. C. Choi, S. Micera, J. Carpaneto, and J. Kim, "Development and quantitative performance evaluation of a noninvasive EMG computer interface," IEEE transactions on bio-medical engineering, vol. 56, pp. 188-191, 2009. https://doi.org/10.1109/TBME.2008.2005950
  6. S. Morita, T. Kondo, and K. Ito, "Estimation of forearm movement from EMG signal and application to prosthetic hand control," Proc. of IEEE International Conference on Robotics and Automation, Yokohama, Japan, vol. 4, pp. 3692-3697, 2001.
  7. J. Rosen, M. Brand, M. B. Fuchs, and M. Arcan, "A myosignal-based powered exoskeleton system," IEEE Transactions on Systems, Man and Cybernetics, Seattle, USA, vol. 31, pp. 210-222, May 2001. https://doi.org/10.1109/3468.925661
  8. R. L. Lieber, Skeletal Muscle Structure, function, & plasticity, Lippincott, 2002.

피인용 문헌

  1. Analysis on Kinematics and Dynamics of Human Arm Movement Toward Upper Limb Exoskeleton Robot Control Part 1: System Model and Kinematic Constraint vol.18, pp.12, 2012, https://doi.org/10.5302/J.ICROS.2012.18.12.1106
  2. Technical Trend of the Lower Limb Exoskeleton System for the Performance Enhancement vol.20, pp.3, 2014, https://doi.org/10.5302/J.ICROS.2014.14.9023
  3. A Study of Sensing Locations for Self-fitness Clothing base on EMG Measurement vol.18, pp.6, 2016, https://doi.org/10.5805/SFTI.2016.18.6.755