김호빈 이종복 김선우 기인호 김상도 박신석 김강건 이종원 2024-01-19T09:30:59Z 2024-01-19T09:30:59Z 2023-06-29 2023-06 1975-6291 https://pubs.kist.re.kr/handle/201004/113653 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. Korean 한국로봇학회 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 Article 10.7746/jkros.2023.18.2.182 2 로봇학회 논문지, v.18, no.2, pp.182 - 188 로봇학회 논문지 18 2 182 188 N kci ART002961927 Gait Phase Estimation Deep Learning LSTM Wearable Walking Robot Exoskeleton