• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.437-439
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• 2008

This paper presents the training system of myoelectric hand prosthesis controlling according to myoelectric signal generated in the human muscle. The training system consist of a trainer, a program for training. The experimental results proved the reliability of proposed training system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.1051-1059
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• 1992

In this paper, two general methods using time-series analysis in the functional separation of the myoelectric signal of human arm movements are developed. Autocorrelation, covariance method and sequential least squares algorithm were used to determine the model parameters and the order of signal model to describe six arm movement patterns` the forearm flexion and extension, the wrist pronation and supination, rotation-in and rotation-out. The confidence interval to classify the functions of arm movement was defined by the mean and standard deviation of total squared error. With the error signals of autoregressive(AR) model, the result showed that the highest success rate was obtained in the case of 4th order, and success rate was decreased with increase of order. Autocorrelation was the method of choice for better success rate. This technique might be applied to biomedical and rehabilitation engineering.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.6
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• pp.67-76
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• 2005

This paper proposes a compact-sized surface myoelectric sensor for the myoelectric hand prosthesis. To fit the surface myoelectric sensor in the socket for 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. The skin interface has one reference and two input electrodes, and the reference electrode is located in the center of two input electrodes. In this paper 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 of the electrodes is the stainless steel (SUS440) that endures sweat and wet conditions. Considering the conduction velocity and the 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 a 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.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2733-2736
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• 2003

This paper proposes a dry-type active surface EMG electrode for the myoelectric prosthetic hand. The designed electrode is small size for embedding in the socket of prosthetic hand, and it has three leads including the reference of signal. To acquire EMG signal rejected the power noise, a precision differential amplifier and various filters such as the band pass filter band rejection filter, low pass and high pass filter are embedded on the electrode. The final output of the electrode is integrated absolute EMG (IEMG) obtained by full rectifier and moving average circuits. From experimental results using the implemented dry-type active surface EMG electrode, the proposed electrode is feasible for the myoelectric prosthetic hand.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.215-220
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• 1995

In this paper, we study myoelectric signal variation through kyungrak needle stimulation. The signal are detected from two kyunrak route of arm; before and after stimulation of a needle, and before and after needle rotation. The detected signals are analyzed at frequency domain to search a characteristic parameters. At the rotation method, spectrum denity of the signals varies large but spectrum is not detected before and after rotation. We can not see any relation bet ween spectrum variation and rotational direction. As the results, when the same stimulation method is used at two different kyungrak route respectively, it is found that the variation of the myoelectric signal is not same.

• Archives of Plastic Surgery
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• v.46 no.4
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• pp.303-310
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• 2019

Background Prosthetic hands with a myoelectric interface have recently received interest within the broader category of hand prostheses, but their high cost is a major barrier to use. Modern three-dimensional (3D) printing technology has enabled more widespread development and cost-effectiveness in the field of prostheses. The objective of the present study was to evaluate the clinical impact of a low-cost 3D-printed myoelectric-interface prosthetic hand on patients' daily life. Methods A prospective review of all upper-arm transradial amputation amputees who used 3D-printed myoelectric interface prostheses (Mark V) between January 2016 and August 2017 was conducted. The functional outcomes of prosthesis usage over a 3-month follow-up period were measured using a validated method (Orthotics Prosthetics User Survey-Upper Extremity Functional Status [OPUS-UEFS]). In addition, the correlation between the length of the amputated radius and changes in OPUS-UEFS scores was analyzed. Results Ten patients were included in the study. After use of the 3D-printed myoelectric single electromyography channel prosthesis for 3 months, the average OPUS-UEFS score significantly increased from 45.50 to 60.10. The Spearman correlation coefficient (r) of the correlation between radius length and OPUS-UEFS at the 3rd month of prosthetic use was 0.815. Conclusions This low-cost 3D-printed myoelectric-interface prosthetic hand with a single reliable myoelectrical signal shows the potential to positively impact amputees' quality of life through daily usage. The emergence of a low-cost 3D-printed myoelectric prosthesis could lead to new market trends, with such a device gaining popularity via reduced production costs and increased market demand.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.4
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• pp.76-84
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• 1993

In this thesis, general method using autoregressive model in the functional separation of the myoelectric signal of human arm movements are suggested. Covariance method and sequential least squares algorithm were used to determine the model parameters and the order of signal model to describe six arm movement patterns` the forearm flexion and extension, the wrist pronation and supination, rotation-in and rotation out. The confidence interval to classify the functions of arm movement was defined by the mean and standard deviation of total squares error. With the error signals of autoregressive(AR) model, the result showed that the highest success, rate was abtained in the case of 4th order, and success rate was decreased with increase of order. This technique might be applied to biomedical-and rehabilitation-engi-neering.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.1070-1073
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• 2002

The biomechanical interaction between the stump and the prosthetic socket is critically important to achieve close-to-normal ambulation. Many investigators suggested that the pressure changes during gait of transfemoral amputees are closely related to the prosthetic alignment, the socket shape, the stump size, and the residual muscle activity. The effects of the prosthetic alignment, the socket shape, and the stump size on the interface pressure were investigated previously. However, there is no report how the residual muscle activities in the transfemoral stump affect the socket interface pressure characteristics during gait. Since designs of socket fur lower limb amputees need to consider the socket interface pressure characteristics, the interface pressure patterns by the residual muscle activities during gait should be investigated. In this study, myoelectric signals (MES) and socket interface pressure in residual limb of transfemoral amputees were measured during the stance and swing phases of gait. For the purpose, specially designed quadrilateral sockets that MES electrodes could be instrumented were fabricated. A total of two transfemoral amputees were participated in the experiments. The measured temporal MES amplitude and interface pressure in knee flexor (biceps femoris) and extensor (rectus femoris) had significant correlations (P ＜ 0.05). Based on the test results, It was suggested that the residual muscle activity of transfemoral amputees stump is an important factor affecting socket pressure changes during walk.

• 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.

• 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.