• 한국지능시스템학회논문지
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• 제17권6호
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• pp.738-743
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• 2007

본 연구는, 팔(하완)을 잃은 장애자용 인공 의수를 장애자가 자신의 의도에 따라 제어하기 위한 센서 시스템과 제어알고리즘에 관한 것이다. 먼저 장애자의 여러 가지 동작 의도를 검출할 수 있는 센싱 시스템을 연구하고 이 센싱 시스템으로부터 발생된 신호를 사용하여 인공의수를 제어하는 방법에 대하여 연구한다. 센서로서는 전기 저항식 굽힘 센서를 사용한다. 이 굽힘 센서를 팔의 상완 이두근과 오구완근에 각각 1개씩 단단히 부착한다. 부착된 센서로부터 출력된 신호는 근육의 굴곡량을 나타내며 팔의 동작의도를 판단 할 수 있는 신호처리 시스템을 통과시켜 하완의 굴곡과 신전 운동, 손의 내전과 외전 운동을 구별한다. 그리고 구별된 신호로부터 실제 팔의 운동 각도를 추정하여 인공의수의 각도를 제어한다. 본 연구의 효용성을 증명하기 위해 2개의 액추에이터와 포텐셔미터를 가진 간단한 인공의수를 제작하여 제어 실험을 하였다. 실험에서 실제 팔의 각도와 인공의수의 제어 각도 사이에는 센서 외부에서 발생되는 노이즈 및 인공의수의 회전 관성, 기계적인 마찰 등으로 인한 오차가 발생하였다. 따라서 오차 값과 오차의 변화 값에 근거한 퍼지 제어 알고리듬을 이용하여 재 실험을 한 결과 하완의 굴곡/신전 운동에서는 평균 약 4도, 손의 회내/외 운동에서는 평균 약 3도의 오차가 측정되어, 퍼지제어기를 설치한 이전보다 오차가 크게 개선되었다.

• 대한전기학회논문지
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• 제43권6호
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• pp.1027-1035
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• 1994

This paper aims to make an artificial arm control strategy. For this, we propose a new feature extraction method and design artificial neural network for the functional classification of myoelectric signal(MES). We first transform the two channel myoelectric signals (MES) for biceps and triceps into frequency domain using fast Fourier transform (FFT). And features were obtained by comparing the magnitudes of ensemble spectrum data and used as inputs to the three-layer neural network for the learning. By changing the number of units in hidden layer of neural network we observed the improvement of classification performance. To observe the effeciency of the proposed scheme we performed experiments for classification of six arm functions to the three subjects. And we obtained on average 94[%] the ratio of classification.

• 한국정밀공학회지
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• 제26권4호
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• pp.82-90
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• 2009

Pneumatic cylinder is one of the low cost actuation sources which have been applied in industrial and prosthetic application since it has a high power/weight ratio, a high-tension force and a long durability However, the control problems of pneumatic systems, oscillatory motion and compliance, have prevented their widespread use in advanced robotics. To overcome these shortcomings, a number of newer pneumatic actuators have been developed such as McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle (PAM) Manipulators. In this paper, one solution for position control of a robot arm, which is driven by two pneumatic artificial muscles, is presented. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external load of the robot arm. To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is proposed in this paper. This estimates the external load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external working loads.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.368-371
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• 2003

A new accurate and reliable human-in-the-loop control by artificial neural network (ANN) for human assistance robot was proposed in this paper. The principle of human-in-the-loop control by ANN was explained including the system architecture of human assistance robot control the design of the controller the control process as well as the switching of the different control patterns. Based on the proposed method, the control of meal assistance robot was implemented. In the controller of meal assistance robote a feedforward ANN controller was designed for the accurate position control. For safety a feedback ANN forcefree control was installed in the meal assistance robot. Both controllers have taken fully into account the influence of human arm upon the meal assistance robote and they can be switched smoothly based on the external force induced by the challenged person arm. By the experimental and simulation work of this method for an actual meal assistance robote the effectiveness of the proposed method was verified.

• 제어로봇시스템학회논문지
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• 제20권9호
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• pp.964-970
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• 2014

In this study we have developed an anthropomorphic robot hand and arm by using tendon-tubes which can be used for people's everyday life as a robot's dynamic power transmission device. Most previous robot hands or arms had critical problem on dynamic optimization due to heavy weight of power transmission parts which placed on robot's finger area or arm area. In order to resolve this problem we designed light-weighted robot hand and arm by using tendon-tubes which were consisted of many articulations and links just like human's hand and arm. The most prominent property of this robot hand and arm is reduction of the weight of robot's power transmission part. Reduction of weight of robot's power transmission parts will allow us to develop energy saving and past moving robot hands and arms which can be used for artificial arms. As a first step for real development in this study we showed structural design and demonstration of simulation of possibility of a robot hand and arm by tendon-tube. In the future research we are planning to verify practicality of the robot hand and arm by applying sensing and controlling method to a specimen.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 D
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• pp.2356-2358
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• 2004

This thesis aims to investigate new approaches to the control strategies of human arm movements and its application for the human interface. By analyzing myoelectric signal(MES) from the arm movements of the normal human subjects, neurological informations obtained patterned could be used to identify different movement patterns of the arm movement. In this paper Artificial neural network for separation of the contraction patterns of four kinds of arm movements, i.e. and flexion and extension of the elbow and adduction and abduction of the forearm were adopted through computer simulation and experiments results were compared with the experimental added-load arm movements.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1983년도 하계학술회의강연.논문초록집
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• pp.141-142
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• 1983

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.799-804
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• 1991

This paper is concerned with the artificial control of prosthetic devices using the electromyographic(EMG) activities of biceps and triceps in human subject during isometric contraction adjustments at the elbow. And it was analysised about recognition of EMG signals and dynamic characteristics at arm movements of human. For this study the error signal of autoregressive(AR) model were used to discriminate arm movement patterns of human. Interaction of dynamic characteristics (Position, Velocity, Acceleration) and EMG of biceps and triceps at arm movements of human was measured.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1714-1717
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• 1997

A human operator is able to perform some tasks smoothly with force feedvack for the teleoperation or a virtual device in a the virtual environments. This paper describes a virtual force generation method with which operator can feel the interactive force between virtula robot and artificial environments. A virtual force generation algortihm is applied to generate the contact force at the arbitrary point of virtual robot, and the virtual force is displayed to the human operator via a tendon master arm consisted with 3 motors. Some experiments has beencarried out to verify the effectiveness of the force generation algorithm and usefulness of the developed backdrivable master arm.

• 한국지능시스템학회논문지
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• 제23권2호
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• pp.172-177
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• 2013

BCI (Brain Computer Interface)는 인간의 뇌에서 측정된 EEG (Electroencephalogram)를 활용하여 의수와 같은 기기를 조종할 수 있는 좋은 방법 중 하나이다. 본 논문에서는 EEG와 힘과의 관계를 알아보고자 최대수축악력 (MVC)의 25%, 50%, 75%로 3개의 등급으로 나누어 EEG 변화를 측정하였다. 얻어진 EEG data를 FFT와 power spectrum analysis로 ${\alpha}$, ${\beta}$, ${\gamma}$파로 나누어 각 파형의 파워 값을 구한 뒤, 구해진 EEG 파워 값을 PCA와 LDA를 사용하여 특징 추출 및 분류를 하였다. 실험 결과 25%의 악력을 가할 때 보다 75%의 악력 때 더 높은 EEG 파워의 증가를 확인하였고, 왼손과 오른손은 각각 52.03%와 77.7%의 분류율을 나타내었다.