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Divergent Component of Motion을 이용한 한쪽 다리 착용로봇의 보행의도감지 방법 개발
오혜원,홍영대 제어로봇시스템학회 2021 제어로봇시스템학회 국내학술대회 논문집 Vol.2021 No.6
This paper proposes a gait intention detection method for a single leg exoskeleton by using Divergent Component of Motion (DCM). Initial measurement units (IMUs) are used for calculating and acquiring the DCM of human while stair ascending. Acquired DCM data are used as input of Neural network (NN) algorithm for pattern recognition. By using trained NN model, stair ascending state is detected in real time. Experiment is carried out to evaluate the performance of proposed method in real walking environment.
착용형 로봇을 제어하기 위한 근경도 기반의 의도 인식 방법
최유나,김준식,이대훈,최영진 한국로봇학회 2023 로봇학회 논문지 Vol.18 No.4
This paper recognizes the motion intention of the wearer using a muscle stiffness sensor and proposes a control system for a wearable robot based on this. The proposed system recognizes the onset time of the motion using sensor data, determines the assistance mode, and provides assistive torque to the hip flexion/extension motion of the wearer through the generated reference trajectory according to the determined mode. The onset time of motion was detected using the CUSUM algorithm from the muscle stiffness sensor, and by comparing the detection results of the onset time with the EMG sensor and IMU, it verified its applicability as an input device for recognizing the intention of the wearer before motion. In addition, the stability of the proposed method was confirmed by comparing the results detected according to the walking speed of two subjects (1 male and 1 female). Based on these results, the assistance mode (gait assistance mode and muscle strengthening mode) was determined based on the detection results of onset time, and a reference trajectory was generated through cubic spline interpolation according to the determined assistance mode. And, the practicality of the proposed system was also confirmed by applying it to an actual wearable robot.
지능형 대퇴 의족 사용자의 의도 검출을 통한제어 모드 변경 기법에 관한 연구
신진우,엄수홍,류중현,이응혁 한국전기전자학회 2020 전기전자학회논문지 Vol.24 No.3
Currently, Intelligent femoral prostheses that support the corresponding mode in walking and specific movements arebeing studied. Certain controls such as upstairs, sitting, and standing require a technique to classify control commandsbased on the user’s intention because the mode must be changed before the operation. Therefore, in this paper, wepropose a technique that can classify various control commands based on the user’s intention in the intelligent thighprosthesis system. If it is determined that the EMG signal needs to be compensated, the proposed technique compensatesthe EMG signal using the correlation between the strength and frequency components of the normal EMG signal and themuscle volume estimated by the pressure sensor. Through the experiment, it was confirmed that the user’s intention wasaccurately detected even in the situation where muscle fatigue was accumulated. Improved intention detection techniquesallow five control modes to be distinguished based on the number of muscle contractions within a given period of time. The results of the experiment confirmed that 97.5% accuracy was achieved through muscle tone compensation even ifthe strength of the muscle signal was different from normal due to muscle fatigue after exercise. 최근 다양한 환경에서의 보행과 특정 동작에서 해당 모드를 지원하는 지능형 대퇴 의족이 개발되고 있다. 계단 상행, 하행과 같은 특정 제어는 동작 전 모드를 변경해야하기 때문에 사용자의 의도를 기반으로 제어명령을 구분하는 기법이 필요하다. 따라서 본 논문에서는 지능형 대퇴 의족 시스템에서 사용자의 의도에 기반하여 다양한 제어명령을 구분할 수 있는 기법을제안한다. 제안하는 기법은 근전도 신호의 보상이 필요하다고 판단되는 경우, 평상시의 근전도 신호의 세기 및 주파수 성분과 압력센서로 추정한 근육의 부피 정도의 상관관계를 이용하여 근전도 신호를 보상하는 것이며 실험을 통해 근피로가 축적되어 있는 상황에도 사용자의 의도를 정확하게 검출하는 것을 확인하였다. 향상된 사용자 의도 검출 기법을 통해 정해진 시간 내 근육의 수축 횟수를 기반으로 5개의 제어모드를 구분할 수 있도록 하였으며 실험 결과 운동 후 근피로로 인해 근신호의 세기가 평시와 다를 경우에도 근신호 보상을 통해 97.5%의 정확도를 갖는 것을 확인하였다.