• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.824-832
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• 2011

An essential consideration to differentiate prosthetic hand from robot hand is its convenience and usefulness rather than high resolution or multi-function of the robot hand. Therefore, this study proposes a myoelectric hand with a 2 DOF auto wrist module which has 6 essential functions of the human hand such as open, grasp, pronation, supination, extension, flexion, which improves the convenience of the daily life. It consists of the 3 main parts, the myoelectric sensor for input signal without additional attachment to operate the prosthetic hand, hand mechanism with high-torqued auto-transmission mechanism and self-locking module which guarantee the safety under the abrupt emergency and minimum power consumption, and dual threshold based controller to make easy for adopting the multi-DOF myoelectric hand. We prove the validity of the proposed system with experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1248-1255
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• 2011

In daily life, another major role of human hand is a communicative function using hand gestures besides grasp function. Therefore, if amputees can express their intention by the prosthetic hand, they can much actively participate in social activities. Thus, this paper propose myoelectric multi-function prosthetic hand which can express 6 useful hand gestures such as Rock, Scissors, Paper, Indexing, Ok and Thumb-up. It was designed as under-actuated structure to minimize volume and weight of the prosthetic hand. Moreover, in order to effectively control various hand gestures by only two EMG sensors, we propose a control strategy that the signal type are expanded as "Strong" and "Light", and hand gestures are hierarchically classified for the intuitive control. Finally, we prove the validity of the developed prosthetic hand with the experiment.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.28-36
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• 2006

In this paper, an algorithm to classify the 4 motions of arm and a control system to position control the prosthesis are studied. To classify the 4 motions, we use flex sensors which is electrical resistance type sensor that can measure warp of muscle. The flex sensors are attached to the biceps brchii muscle and coracobrachialis muscle and the sensor signals are passed the sensing system. 4 motion of the forearm - flexion and extension, the pronation and supination are classified from this. Also position of forearm is measured from the classified signals. Finally, A two D.O.F prosthesis arm with RC servo-motor is designed to verify the validity of the algorithm. At this time, fuzzy controller is used to reduce the position error by rotary inertia and noise. From the experiment, the position error had occurred within about 5 degree.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.697-708
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• 1996

In this paper, we study reactive congestion control with priority in ATM network. The priority schemes for buffer access, partial buffer sharing have been investigated in order to improve the utilization of ATM network resources the network and to satisfy the most demanding traffic class. We consider in this paper a discrete-time queueing model for partial buffer sharing with two Markov modulated Poisson inputs. This model can be used to analyze the the effects of the partial buffer sharing priority scheme on system performance for realistic cases of bursty services. Explicit formulae are derived for the number of cells in the system and the loss probabilities for the traffic. Congestion may still occur because of unpredictable statistical fluctuation of traffic sources even when preventive control is performed in the network. In this Paper, we study reactive congestion control, in which each source changes its cell emitting rate a daptively to the traffic load at the switching node. Our intention is that,by incorporating such a congcstion control method in ATM network,more efficient congsestion control is established. We develope an analytical model,and carry out an approximateanalysis of reactive congestion con-trol with priority.Numerical results show that several orders of magnitude improvement in the loss probability can be achieved for the high priority class with little impact on the low priority class performance.And the results show that the reactive congestion control with priority are very effective in avoiding congestion and in achieving the statistical gain.

• Water for future
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• v.18 no.2
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• pp.153-161
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• 1985

The methods for hydrologic prediction of streamflows for more efficient and functional operations and automation of the flood warning and forecasting system have been studiedand which have been widely used in the control engineering have been studied and investigated for representation of the dynamic behavior of rainfall-runoff precesses, and formulated into mathematical model form. The applicabilities of the model using the adaptive Kalman filter algorithm to the on-line, real-time prediction of river flows have been worked out. The computer programs in FORTRAN which are developed here can be utilized for more efficient operations and better prediction abilities of flood warning and forecasting systems, and also should be modified for better model performance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.214-221
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• 2007

This paper presents a myoelectric hand prosthesis(MHP) with two degree of freedom(2-DOF), which consists of a mechanical hand, a surface myoelectric sensor(SMES) for measuring myoelectric signal, a control system and a charging battery. The actuation for the 2-DOF hand functions such as grasping and wrist rotation was performed by two DC-motors, and controlled by myoelectric signal measured from the residual forearm muscle. The grip force of the MHP was automatically changed by a mechanical automatic speed reducer mounted on the hand. The skin interface of SMES was composed of the electrodes using the SUS440 metal in order to endure a wet condition due to the sweat. The sensor was embedded with a amplifier and a filter circuit for rejecting the offset voltage caused by power line noises. The control system was composed of the grip force sensor, the slip sensor, and the two controllers. The two controllers were made of a RISC-type microprocessor, and its software was executed on a real-time kernel. The control system used Force Sensing Resistors, FSR, as slip pick-ups at the fingertip of a thumb and the grip force information was obtained from a strain-gauge on the lever of the MHP. The experimental results were showed that the proposed control system is feasible for the MHP.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.465-474
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• 1994

This study is concerned with a method which helps human to generate EMG signals accurately and consistently to make reliable design samples of function discriminator for prosthetic arm control. We intend to ensure a signal accuracy and consistency by training human as a signal generation source. For the purposes, we construct a human training system using a digital computer, which generates visual graphes to compare real target motion trajectory with the desired one, to observe EMG signals and their features. To evaluate the effect which affects a feature variance and a feature separability between motion classes by the human training system, we select 4 features such as integral absolute value, zero crossing counts, AR coefficients and LPC cepstrum coefficients. We perform a experiment four times during 2 months. The experimental results show that the hu- man training system is effective for accurate and consistent EMG signal generation and reduction of a feature variance, but is not correlated for a feature separability, The cepstrum coefficient is the most preferable among the used features for reduction of variance, class separability and robustness to a time varing property of EMG signals.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.891-894
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• 2005

This paper describes a human finger model driven by shape memory alloy(SMA) wires. The finger model has three joints that are similar to human finger. Each joint is actuated with two wires in the antagonistic manner and six wires are used to actuate three finger joint. In order to obtain the desirable finger motion, the diameters of the SMA wires are designed with different diameters by considering the required actuating force and response time. The rotary sensors are used to measure the angle positions of the joints and PWM control using PID algorithm is used to achieve desired angle positions of the finger joints. After estimating the control performance of each finger joint for the desired angle position, the antagonistic motion control of the finger model is experimentally evaluated.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.265-271
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• 2018

The recent prosthetic technologies pursue to control multi-DOFs (degrees-of-freedom) hand and wrist. However, challenges such as high cost, wear-ability, and motion intent recognition for feedback control still remain for the use in daily living activities. The paper proposes a multi-channel knit band sensor to worn easily for surface EMG-based prosthetic control. The knitted electrodes were fabricated with conductive yarn, and the band except the electrodes are knitted using non-conductive yarn which has moisture wicking property. Two types of the knit bands are fabricated such as sixteen-electrodes for eight-channels and thirty-two electrodes for sixteen-channels. In order to substantiate the performance of the biopotential signal acquisition, several experiments are conducted. Signal to noise ratio (SNR) value of the knit band sensor was 18.48 dB. According to various forearm motions including hand and wrist, sixteen-channels EMG signals could be clearly distinguishable. In addition, the pattern recognition performance to control myoelectric prosthesis was verified in that overall classification accuracy of the RMS (root mean squares) filtered EMG signals (97.84%) was higher than that of the raw EMG signals (87.06%).

• Journal of IKEEE
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• v.24 no.4
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• pp.1039-1049
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• 2020

Neural decoding is important to recognize the user's intention for controlling a neuro-prosthetic hand. This paper proposes a real-time decoding method for multi-channel peripheral neural activity. Peripheral nerve signals were measured from the median and radial nerves, and motion artifacts were removed based on locally fitted polynomials. Action potentials were then classified using a k-means algorithm. The firing rate of action potentials was extracted as a feature vector and its dimensionality was reduced by a self-organizing feature map. Finally, a multi-layer perceptron was used to classify hand motions. In monkey experiments, all processes were completed within a real-time constrain, and the hand motions were recognized with a high success rate.