• International Journal of Precision Engineering and Manufacturing
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• v.6 no.3
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• pp.37-44
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• 2005

Gait control capacity for most trans-femoral prostheses is significantly different from that of a normal person, and training is required for a long period of time in order for a patient to walk properly. People become easily tired when wearing a prosthesis or orthosis for a long period typically because the gait angle cannot be smoothly adjusted during wearing. Therefore, to improve the gait control problems of a trans-femoral prosthesis, the proper gait angle is estimated through surface EMG(electromyogram) signals on a normal leg, then the gait posture which the trans-femoral prosthesis should take is calculated in the neural network, which learns the gait kinetics on the basis of the normal leg's gait angle. Based on this predicted angle, a postural control method is proposed and tested adaptively following the patient's gait habit based on the predicted angle. In this study, the gait angle prediction showed accuracy of over $97\%$, and the posture control capacity of over $90\%$.

• Journal of Biomedical Engineering Research
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• v.16 no.3
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• pp.317-324
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• 1995

A need exists for the accurate identification of time series models having time varying parameters, as is important in the case of real time identification of nonstationary EMG signal. Thls paper describes real time identification and muscle fatigue monitoring method of nonstationary EMG signal. The method is composed of the efficient identifier which estimates the autoregressive parameters of nonstationary EMG signal model, and its real time implementation by using T805 parallel processing computer. The method is verified through experiment with real EMG signals which are obtained from surface electrode. As a result, the proposed method provides a new approach for real time Implementation of muscle fatigue monitoring and the execution time is 0.894ms/sample for 1024Hz EMG signal.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1497-1505
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• 2010

The present study was performed to investigate the effect of aging on muscular activity of older subjects under the condition of fixed load muscle contraction. SEMG signals were recorded from old (46 women) and young (45 women) groups performing sustained isometric elbow flexion contraction with a fixed load during 30 seconds. Linear regression and mean square error (MSE) analysis with four characteristic variables (ARV, RMS, MDF, MNF) were used to compare the age-related difference (of local muscle fatigue and fluctuation of the amplitude and frequency) in the SEMG signal. The main results can be summarized as follows: During sustained muscle contraction with a fixed load: i) the MSE values of amplitude (ARV, RMS) and frequency (MDF, MNF) variables were more than 30% higher for the young than for the old adults; ii) the measures of local muscle fatigue (slope of the MDF and MNF) indicated greater fatigue in the old; and iii) the rate of increase of the SEMG amplitude was higher for the young than the old.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.10
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• pp.1779-1785
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• 2006

This paper presents a method of virtual robot arm control by EMG pattern recognition using the proposed hybrid system. The proposed hybrid system is composed of the LVQ and the SOFM, and the SOFM is used for the preprocessing of the LVQ. The SOFM converts the high dimensional EMG signals to 2-dimensional data. The EMG measurement system uses three surface electrodes to acquire the EMG signal from operator. Six hand gestures can be classified sufficiently by the proposed hybrid system. Experimental results are presented that show the effectiveness of the virtual robot arm control by the proposed hybrid system based classifier for the recognition of hand gestures from EMG signal patterns.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.378-381
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• 2002

In this paper, we proposed an electromyography(EMG) based control method of a virtual robot arm as an adaptive human supporting system or remote control system, which consists of an shoulder control part, elbow control part, and wrist control part. The system uses four surface electrodes to acquire the EMG signal from operator. It is shown from the experiments that the EMG patterns during arm motions can be classified sufficiently by using SOM and LVQ. The interface system based on PC environment is constructed to 3-D graphic user interface(GUI) program. Experimental results show that proposed method obtains approximately 94 percent of success in classification.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.1
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• pp.20-25
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• 2012

Gender differences have been studied by using spectral features such as median frequency (MDF) respectively. MDF is the most commonly used as a feature to describe muscle conduction velocity. The aim of this paper is to detect gender differences from surface EMG signals during isotonic contractions of the bicep Brachii. Eight volunteers participated in surface EMG recordings placed on the biceps brachii and each recording experiment continued until their exhaustion. We used feature values and regressive slopes and compared the feature changes from the onset to the endurance time to find gender differences. The result of experiments shows that the regressive slope of these features is valid to measure gender differences.

• Journal of Biomedical Engineering Research
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• v.33 no.2
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• pp.65-71
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• 2012

In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.5
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• pp.338-347
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• 2005

A computer-based low back muscle evaluation system was designed to simultaneously acquire, process, display, quantify, and correlate electromyographic(EMG) activity with muscle force, and range of motion(ROM) in the lumbar muscle of human. This integrated multi-channel system was designed around notebook PC. Each channel consisted of a time and frequency domain block, and T-F(time-frequency) domain block. The captured data in each channel was used to display and Quantify : raw EMG, histogram, zero crossing, turn, RMS(root mean square), variance, mean, power spectrum, median frequency, mean frequency, wavelet transform, Wigner-Ville distribution, Choi-Williams distribution, and Cohen-Posch distribution. To evaluate the performance of the designed system, the static and dynamic contraction experiments from lumbar(waist) level of human were done. The experiment performed in five subjects, and various parameters were tested and compared. This system could equally well be modified to allow acquisition, processing, and analysis of EMG signals in other studies and applications.

• Journal of Electrical Engineering and Technology
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• v.7 no.3
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• pp.451-458
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• 2012

The voluntary EMG (vEMG) signal from electrically stimulated muscle is very useful for feedback control in functional electrical stimulation. However, the recorded EMG signal from surface electrodes has unwanted stimulation artifact and M-wave as well as vEMG. Here, we propose an event-synchronous adaptive digital filter for the suppression of stimulation artifact and M-wave in this application. The proposed method requires a simple experimental setup that does not require extra hardware connections to obtain the reference signals of adaptive digital filter. For evaluating the efficiency of this proposed method, the filter was tested and compared with a least square (LS) algorithm using previously measured data. We conclude that the cancellation of both primary and residual stimulation artifacts is enhanced with an event-synchronous adaptive digital filter and shows promise for clinical application to rehabilitate paretic limbs. Moreover because this algorithm is far simpler than the LS algorithm, it is portable and ready for real-time application.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.694-698
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• 1987

In this paper, the primitive and double combined motion classification of the arm is discussed using pattern recognition of EM signal. The EM signals are detected from Ag-Ag/Cl surface electrodes, and IBM PC, calculated the Likelyhood probability and the decision function on the feature space of integral absolute value. Multiclass decision rule is introduced for higher decision rate. On our experimental results from expert simulator, the decision rate of more than 78% can be obtained.