• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.3
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• pp.248-257
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• 2007

This paper presents the control system for driving myoelectric hand prosthesis according to myoelectric signal generated in the human muscle. A surface myoelectric sensor for measuring myoelectric signal is designed a skin interface and a processing circuit according to myoelectric signal output property. The control system consists of two controller for driving dual motor, torque sensor for measuring out torque of motor, slip sensor for detecting slip of torque. The experimental results proved the proposed control system.

• 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.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.340-342
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• 2007

This paper presents the grip force control of myoelectric hand prosthesis according to myoelectric signal generated in the human muscle. The control system consist of a controller for driving DC motor, torque sensor for measuring out torque of motor, slip sensor for detecting slip of torque. The experimental results proved the reliability of proposed control system.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.250-252
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• 2007

In this paper presents a control algorism for myoelectric hand prosthesis(MHP) with 2 degree of freedom(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 two controllers were made of a RISC-type microprocessor, and its software was executed on a real-time kernel. The experimental results were showed that the proposed a control algorism is feasible for the MHP.

• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.243-249
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• 2005

The number of people who have lost limbs due to amputation has increased due to various accidents and diseases. Numerous attempts have been made to provide these people with prosthetic devices. These devices are often controlled using myoelectric signals. Although the success of fitting myoelectric signals (EMG) for single device control is apparent, extension of this control to more than one device has been difficult. The lack of success can be attributed to inadequate multifunctional control strategies. Therefore, the objective of this study was to develop multifunctional myoelectric control strategies that can generate a number of output control signals. We demonstrated the feasibility of a neural network classification control method that could generate 12 functions using three EMG channels. The results of evaluating this control strategy suggested that the neural network pattern classification method could be a potential control method to support reliability and convenience in operation. In order to make this artificial neural network control technique a successful control scheme for each amputee who may have different conditions, more investigation of a careful selection of the number of EMG channels, pre-determined contractile motions, and feature values that are estimated from the EMG signals is needed.

• Proceedings of the KIPE Conference
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• 2010.07a
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• pp.265-266
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• 2010

This paper propose the test method for performance of myoelectric hand prosthesis(MHP) according to myoelectric signal generated in the human muscle. The MHP consists of a mechanical hand, a surface myoelectric sensor(SMES) for measuring myoelectric signal, a control system and a charging battery. The propose test method is proved the reliability of MHP.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.9
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• pp.935-944
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• 2006

EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMC pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMC signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure, and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time pattern recognition system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the generation of control commands for myoelectric hand, are completed within 97 msec. These results confirm that our method is applicable to real-time EMG pattern recognition far myoelectric hand control.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1268-1271
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• 2005

This paper proposes a compact-sized surface myoelectric sensor for myoelectric hand prosthesis. To fit the surface myoelectric sensor in the socket of the myoelectric hand prosthesis, the sensor should be a compact size. The surface myoelectric sensor is composed of a skin interface and a single processing circuit that are mounted on a single package. Since the skin interface has one reference and two input electrodes, and the reference electrode is located in middle of two input electrodes, we propose two types of sensors with the circle- and bar-shaped reference electrode, but all input electrodes are the bar-shaped. The metal material used for the electrodes is the stainless steel (SUS440) that endures sweat and wet conditions. Considering conduction velocity and median frequency of the myoelectric signal, we select the inter-electrode distance (IED) between two input electrodes as 18mm, 20mm, and 22 mm. The signal processing circuit consists of a differential amplifier with band pass filter, a band rejection filter for rejecting 60Hz power-line noise, amplifiers, and a mean absolute value circuit. We evaluate the proposed sensor from the output characteristics according to the IED and the shape of the reference electrode. From the experimental results we show the surface myoelectric sensor with the 18mm IED and the bar-shaped reference electrode is suitable for the myoelectric hand prosthesis.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.11-15
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• 2011

This paper propose the test method for a performance of the myoelectric hand prosthesis(MHP) controlled according to the myoelectric signal generated in the human muscle. The MHP consists of a mechanical hand, a surface myoelectric sensor(SMES) for a measuring myoelectric signal, a control system and a charging battery. The two commercialized MHP is tested for the grip endurance property. The test results is not difference a noise and a grip force. The proposed test method is proved the reliability of MHP by the endurance test.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.842-847
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• 2005

This paper proposes a novel real-time EMG pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction virtual hand. From experimental results, we show that all processes, including virtual hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.