본문 바로가기
KERD
논문KCI 등재

평지 및 계단 환경에서 보행 속도 변화에 대응 가능한 웨어러블 로봇의 보행 위상 추정 방법

Gait Phase Estimation Method Adaptable to Changes in Gait Speed on Level Ground and Stairs

0

조회

0

다운로드

1

인용

0

참고문헌

키워드

초록

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

Abstract

Due to the acceleration of an aging society, the need for lower limb exoskeletons to assist gait is increasing. And for use in daily life, it is essential to have technology that can accurately estimate gait phase even in the walking environment and walking speed of the wearer that changes frequently. In this paper, we implement an LSTM-based gait phase estimation learning model by collecting gait data according to changes in gait speed in outdoor level ground and stair environments. In addition, the results of the gait phase estimation error for each walking environment were compared after learning for both max hip extension (MHE) and max hip flexion (MHF), which are ground truth criteria in gait phase divided in previous studies. As a result, the average error rate of all walking environments using MHF reference data and MHE reference data was 2.97% and 4.36%, respectively, and the result of using MHF reference data was 1.39% lower than the result of using MHE reference data.

참고문헌 (12)

  1. Ho-Bin Kim, "LSTM based Gait Phase Estimation Method Robust to Changes in Gait Speed", Korea Information Processing Society Conference, pp.429~431 (2022), 한국정보처리학회
  2. Keehong Seo, "Fully autonomous hip exoskeleton saves metabolic cost of walking", 2016 IEEE International Conference on Robotics and Automation (ICRA) (2016), IEEE
  3. Dong Jin Hyun, "Walking propulsion generation in double stance by powered exoskeleton for paraplegics", Robotics and Autonomous Systems, 116, pp.24~37 (2019), Elsevier BV[DOI]
  4. H. Hara, "Development of HAL for lumbar support", SCIS & ISIS SCIS & ISIS 2010 (2010)
  5. Younbaek Lee, "Flexible Gait Enhancing Mechatronics System for Lower Limb Assistance (GEMS L-Type)", IEEE, 2019, pp.24(4), 1520~1531[DOI]
  6. Jinsoo Kim, "Reducing the metabolic rate of walking and running with a versatile, portable exosuit", Science, 365(6454), pp.668~672 (2019), American Association for the Advancement of Science (AAAS)[DOI]
  7. Kai Schmidt, "The Myosuit: Bi-articular Anti-gravity Exosuit That Reduces Hip Extensor Activity in Sitting Transfers", Frontiers in Neurorobotics, 11 (2017), Frontiers Media SA[DOI]
  8. Inseung Kang, "Real-Time Neural Network-Based Gait Phase Estimation Using a Robotic Hip Exoskeleton", IEEE Transactions on Medical Robotics and Bionics, 2(1), pp.28~37 (2020), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
  9. Keehong Seo, "A new adaptive frequency oscillator for gait assistance", 2015 IEEE International Conference on Robotics and Automation (ICRA) (2015), IEEE
  10. Jinwon Lee, "Continuous Gait Phase Estimation Using LSTM for Robotic Transfemoral Prosthesis Across Walking Speeds", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29, pp.1470~1477 (2021), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
  11. Inseung Kang, "Real-Time Gait Phase Estimation for Robotic Hip Exoskeleton Control During Multimodal Locomotion", IEEE Robotics and Automation Letters, 6(2), pp.3491~3497 (2021), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
  12. Yuepeng Qian, "Predictive Locomotion Mode Recognition and Accurate Gait Phase Estimation for Hip Exoskeleton on Various Terrains", IEEE Robotics and Automation Letters, 7(3), pp.6439~6446 (2022), Institute of Electrical and Electronics Engineers (IEEE)[DOI]

관련 논문AI추천

이 간행물의 다른 논문 (10건)