전자공학회논문지 (Journal of the Institute of Electronics and Information Engineers)

  • 제52권3호
  • /
  • Pages.181-189
  • /
  • 2015
  • /
  • 2287-5026(pISSN)
  • /
  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지
DOI QR코드

DOI QR Code

상·하향 계단보행을 위한 근전도 신호 기반 보행단계 인식

Gait Phase Recognition based on EMG Signal for Stairs Ascending and Stairs Descending

  • Lee, Mi-Ran (Department of Electronic Engineering, Inha University) ;
  • Ryu, Jae-Hwan (Department of Electronic Engineering, Inha University) ;
  • Kim, Sang-Ho (Department of Electronic Engineering, Inha University) ;
  • Kim, Deok-Hwan (Department of Electronic Engineering, Inha University)
  • 투고 : 2014.10.15
  • 심사 : 2015.02.27
  • 발행 : 2015.03.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

동력의족은 하지 절단 환자나 다리근력이 부족한 사람들의 보행 보조를 위해 사용된다. 동력의족의 자연스러운 구동을 위해 선 보행단계가 잘 분류되어야 한다. 물리센서를 이용하여 보행단계를 분류하는 기존 연구는 동력의족이 사전에 훈련된 보행속도로만 재현되는 단점이 있다. 따라서 본 논문에서는 물리센서를 사용하지 않고, 근전도 신호만을 이용하여 오르막, 내리막 계단보행을 각각 4단계로 분류하는 방법을 제안한다. 근전도 신호를 RMS, VAR, MAV, SSC, ZC, WAMP 특징으로 산출하여 LDA(Linear Discriminant Analysis) 분류기를 통해 보행단계를 인식한다. 훈련 단계에서는 AHRS센서를 이용하여 무릎각도 변화에 따른 보행단계 범위를 생성한다. 실험 결과, 선행 연구의 경우 오르막 보행에서 평균 58.5%, 내리막 보행에서 35.3%의 정확도를 보인다. 반면, 제안하는 방법은 오르막 보행에서 평균 85.6%, 내리막 보행에서 69.5%의 인식률을 보인다. 또한, 본 연구를 통해 개별 근육 별 보행단계 평균 인식률을 분석하였다.

Powered prosthesis is used to assist walking of people with an amputated lower limb and/or weak leg strength. The accurate gait phase classification is indispensable in smooth movement control of the powered prosthesis. In previous gait phase classification using physical sensors, there is limitation that powered prosthesis should be simulated as same as the speed of training process. Therefore, we propose EMG signal based gait phase recognition method to classify stairs ascending and stairs descending into four steps without using physical sensors, respectively. RMS, VAR, MAV, SSC, ZC, WAMP features are extracted from EMG signal data and LDA(Linear Discriminant Analysis) classifier is used. In the training process, the AHRS sensor produces various ranges of walking steps according to the change of knee angles. The experimental results show that the average accuracies of the proposed method are about 85.6% in stairs ascending and 69.5% in stairs descending whereas those of preliminary studies are about 58.5% in stairs ascending and 35.3% in stairs descending. In addition, we can analyze the average recognition ratio of each gait step with respect to the individual muscle.

키워드

참고문헌

  1. H-J. Cho, K-H. Kang, H-C. Park, M-S. Kang, Y-S. Choi, T-K. Kim, S-J. Yoon, "Effect of Inclined Backward and Forward Walking Training on Muscle Strength and Electromyographic Activity", Journal of the Society of Living Environment System of Korea, Vol.16, No.2, pp186-193, April 2009.
  2. J-Y. Jung, Y-S. Yang, Y-W. Won, J-J Kim, "Development of Wireless Ambulatory Measurement System based on Inertial Sensors for Gait Analysis and its Application for Diagnosis on Elderly People with Diabetes Mellitus", Journal of the Institute of Electronics Engineers of Korea, Vol.48, No.2, pp38-46, March 2011.
  3. J-Y. Lee, K-J. Lee, Y-H. Kim, S-H. Lee, S-W. Park, "Development of Gait Analysis Algorithm for Hemiplegic Patients based on Accelerometry", Journal of the Institute of Electronics Engineers of Korea, Vol.41, No.4, pp.231-240, July 2004.
  4. M-G. Chae, J-Y. Jung, C-J. Park, I-H. Jang, H-S. Park, "Gait Phases Classification using Joint angle and Ground Reaction Force: Application of Backpropagation Neural Networks", Journal of Institute of Control & Robotics and Systems, Vol.18, No.7, pp.644-649, July 2012. https://doi.org/10.5302/J.ICROS.2012.18.7.644
  5. J-H. Ryu, D-H. Kim, "sEMG Signal Based Gait Phase Recognition Method for Selecting Features and Channels Adaptively", Journal of The Rehabilitation Engineering and Assistive Technology Society, Vol.7, No.2, pp.19-26, December 2013.
  6. Alison C. Novak, Samantha M. Reid, Patrick A. Costigan, Brenda Brouwer "Stair negotiation alters stability in older adults", Lower Extremity Review, October 2010
  7. H-S. Cho, Y-H. Chang, J-C Ryu, M-S. Mun, C-B. Kim, "Analysis of Stair walking characteristics for the development of Exoskeletal walking Assist Robot", Journal of The Rehabilitation Engineering and Assistive Technology Society of Korea, Vol.6, No.2, pp.15-22, December 2012.
  8. E-S. Kim, "Trajectory Generation Schemes for Bipedal Ascending and Descending Stairs using Genetic Algorithm(GA)", University of Dong-A, February 2010.
  9. H.Huang, T.A. Kuiken and R.D. Lipschutz, "A Strategy for Identifying Locomotion Modes Using Surface Electromyography," The Journal of Biomedical Engineering, IEEE Transactions on, Vol. 56, No.1, pp.65-73,January 2009. https://doi.org/10.1109/TBME.2008.2003293
  10. A.Phinyomark, S.Hirunviriya, C.Limsakul, P.Phukpattaranont, "Evaluation of EMG Feature Extraction for Hand Movement Recognition Based on Euclidean Distance and Standard Deviation", in Proc. of ECTI-CON Conf. on 2010 International Conference, pp.856-860, Chiang Mai, Thailand, May 2010.
  11. Boris I. Prilutsky, Ludmila N. Petrova, Leonid M. Raitsin, "Comparison of Mechanical energy expenditure of joint moments and muscle forces during human locomotion", The Journal of Biomechanics, Vol.29, No.4, pp.405-415, April 1996. https://doi.org/10.1016/0021-9290(95)00083-6
  12. S-H. Kim, J-H. Ryu, D-H. Kim, "Gait phase classification for Stair walking using Feature Extraction and Muscle selection based on EMG Signals", in Proc. of IEEK Conf. on Summer Conference, Vol.37, No.1, pp.1053-1056, Jeju, Korea, June 2014.
  13. A.Phinyomark, P.Phukpattaranont, C.Limsakul, "Feature reduction and selection for EMG signal classification", The Journal of Expert Systems with Applications, Vol.39, No.8, pp.7420-7431, June 2012. https://doi.org/10.1016/j.eswa.2012.01.102

피인용 문헌

  1. Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network vol.2020, pp.None, 2015, https://doi.org/10.1155/2020/5684812