• 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.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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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.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.4
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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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.9
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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.

• Proceedings of the KIEE Conference
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• 2004.07d
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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.

• Proceedings of the KIEE Conference
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• 1983.07a
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• pp.141-142
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• 1983

• Proceedings of the KIEE Conference
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• 1991.07a
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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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• 1997.10a
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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.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.2
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• pp.172-177
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• 2013

With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electroencephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is necessary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.