• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1944-1947
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• 2005

In this paper, the control of a prosthetic arm using the flex sensor signal is described. The flex sensors are attached to the biceps and triceps brchii muscle. The signals are passed a differential amplifier and noise filter. And then the signals are converted to digital data by PCI 6036E ADC. From the data, position and velocity of arm joint are obtained. Also motion of the forearm - flexion and extension, the pronation and supination are abstracted from the data by proposed algorithm. A two D.O.F arm with RC servo-motor is designed for experiment. The arm length is 200 mm, weight is 4.5 N. The rotation angle of elbow joint is $120^{\circ}$. Also the rotation angle of the wrist is $180^{\circ}$. Through the experiment, we verified the possibility of the prosthetic arm control using the flex sensor signal. We will try to improve the control accuracy of the prosthetic arm continuously.

• 전기의세계
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• v.33 no.7
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• pp.412-418
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• 1984

의용생체공학의 한분야인 재활공학의 많은 발전으로 상실된 인간의 사지기관의 일부는 거의 자연스런 기능을 갖는 장치로 대치할 수 있는 가능성이 높아지고 있으며, 일한 연구의 결과는 산업용 로보트의 개발에도 기여를 하고 있다. 그중에서도 핵심이 되고 있는 것이 근전도신호를 이용한 보철제어(Prosthesis Control)에 관한 연구이다. 근전도신호가 인공팔제어에 이용된 것은 1950년대 초 소련에서 처음 시도되었고 그후 유럽, 카나다 미국등에서 계속 이에 관한 연구가 성과를 나타내고 있다. 근전도 신호를 제어신호로 사용할 경우 가장 큰 문제점은 근전도신호의 저주파 잡음인데, 실제로 비례제어신호를 얻기위하여는 이 잡음이 제거되어야 한다. 그러므로 여기에서는 근전도신호 처리방법에 대한 개략적인 것을 소개하고, 잡음의 제거방법등을 검토해 보고자 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.738-743
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• 2007

In this paper, a muscle motion sensing system and an artificial arm control system are studied. The artificial arm is for the people who lost one's forearm. The muscle motion sensing system detect the intention of motion from the upper arm's muscle. In sensing system we use flex sensors which is electrical resistance type sensor. The sensor is attached on the biceps brachii muscle and coracobrachialis muscle of the upper arm. We propose an algorithm to classify the one's intention of motions from the sensor signal. Using this algorithm, we extract the 4 motions which are flexion and extension of the forearm, pronation and supination of the arm. To verify the validity of the proposed algorithms we made experiments with two d.o.f. artificial arm. To reduce the control errors of the artificial arm we also proposed a fuzzy PID control algorithm which based on the errors and error rate.