• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.641-642
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• 1986

We present a method for the estimation of intramuscular electromyographic(EMG) signals from the given surface EMG signals. This method is based on representing the surface EMG signal as an autoregressive(AR) time model with a delayed intramuscular EMG signal as an input. The parameters of the time series model that transforms the intramuscular signal to the surface signal are identified. The identified model is then used in estimating the intramuscular signal from the surface signal.

• Journal of the Ergonomics Society of Korea
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• v.19 no.2
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• pp.87-99
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• 2000

This study aims to examine a fuzzy logic-based human expert EMG prediction model (FLHEPM) for predicting electromyographic responses of trunk muscles due to manual lifting based on two task (control) variables. The FLHEPM utilizes two variables as inputs and ten muscle activities as outputs. As the results, the lifting task variables could be represented with the fuzzy membership functions. This provides flexibility to combine different scales of model variables in order to design the EMG prediction system. In model development, it was possible to generate the initial fuzzy rules using the neural network, but not all the rules were appropriate (87% correct ratio). With regard to the model precision, the EMG signals could be predicted with reasonable accuracy that the model shows mean absolute error of 8.43% ranging from 4.97% to 13.16% and mean absolute difference of 6.4% ranging from 2.88% to 11.59%. However, the model prediction accuracy is limited by use of only two task variables which were available for this study (out of five proposed task variables). Ultimately, the neuro-fuzzy approach utilizing all five variables to predict either the EMG activities or the spinal loading due to dynamic lifting tasks should be developed.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.15 no.1
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• pp.61-70
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• 2005

This study investigated the spinal loads(L5/S1 disc compression and shear forces) predicted from four biomechanical models: one EMG model and three optimization models. Three objective functions used in the optimization models were to miminize 1) the cubed muscle forces : MF3, 2) the cubed muscle stress : MS3, 3) maximum muscle intensity : MI. Twelve healthy male subjects participated in the isometric voluntary exertion tests to six directions : flexion/extension, left/right lateral bending, clockwise/ counterclockwise twist. EMG signals were measured from ten trunk muscles and spinal loads were assessed at 10, 20, 30, 40, 50, 60, 70, 80, 90%MVE(maximum voluntary exertion) in each direction. Three optimization models predicted lower L5/S1 disc compression forces than the EMG model, on average, by 31%(MF3), 27%(MS3), 8%(MI). Especially, in twist and extension, the differences were relatively large. Anterior-posterior shear forces predicted from optimization models were lower, on average, by 27%(MF3), 21%(MS3), 9%(MI) than by the EMG model, especially in flexion(MF3 : 45%, MS3 : 40%, MI : 35%). Lateral shear forces were predicted far less than anterior-posterior shear forces(total average = 124 N), and the optimization models predicted larger values than the EMG model on average. These results indicated that the optimization models could underestimate compression forces during twisting and extension, and anterior-posterior shear forces during flexion. Thus, future research should address the antagonistic coactivation, one major reason of the difference between optimization models and the EMG model, in the optimization models.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.11
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• pp.541-549
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• 2001

The term 'silent period(SP)' refers to a transitory, relative or absolute decrease electromyography(EMG) activity, evoked in the midst of an otherwise sustained contraction. Masseteric SP is elicited by a tap on the chin during isometric contraction of masseter muscle. In this paper, a new EMG signal generation model including SP in masseter muscle is proposed. This work is based on the anatomical structure of trigeminal nerve system that related on temporomandibular joint(TMJ) dysfunction. And it was verified by comparing the real EMG signals including SP in masseter muscle to the simulated signals by the proposed model. Through this studies, it was shown that SP has relation to variable neurophysiological phenomena. A proposed model is based on the control system theory and DSP(Digital Signal Processing) theory, and was simulated using MATLAB simulink. As a result, the proposed SP model generated EMG signals which are similar to real EMG signal including normal SP and an abnormal extended SP. This model can be applied to the diagnosis of TMJ dysfunction and can effectively explain the origin of extended SP.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.9-15
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• 2008

Unlike robotic systems, humans excel at a variety of tasks by utilizing their intrinsic impedance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulators human''s superior motor skills in contact tasks. This paper develops a novel method for estimating and predicting the human joint impedance using the electromyogram(EMG) signals and limb position measurements. The EMG signal is the summation of MUAPs (motor unit action potentials). Determination of the relationship between the EMG signals and joint stiffness is difficult, due to irregularities and uncertainties of the EMG signals. In this research, an artificial neural network(ANN) model was developed to model the relation between the EMG and joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. The feasibility of the developed model was confirmed by experiments and simulations.

• Journal of Biomedical Engineering Research
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• v.27 no.3
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• pp.101-109
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• 2006

It has been known that there is strong relationship between human voices and the movements of the articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The EMG signals were acquired from three articulatory facial muscles. Preliminary, 10 Korean digits were used as recognition variables. The various feature parameters including filter bank outputs, linear predictive coefficients and cepstrum coefficients were evaluated to find the appropriate parameters for EMG-based speech recognition. The sequence of the EMG signals for each word is modelled by a hidden Markov model (HMM) framework. A continuous word recognition approach was investigated in this work. Hence, the model for each word is obtained by concatenating the subword models and the embedded re-estimation techniques were employed in the training stage. The findings indicate that such a system may have a capacity to recognize speech signals with an accuracy of up to 90%, in case when mel-filter bank output was used as the feature parameters for recognition.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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• pp.280-291
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• 1987

In this paper, the linear autoregressive model of EMG signal for four basic arm functions was presented and parameters for each function were estimated. The signal identification was carried out using function discrimination algorithm. It was validated that EMG signal was a widesense stationary process and the linear autoregressive model of EMG signal was constructed through approximating it to Gaussian process. It was confined that Levinson-Durbin algoridthm is a more appropriate one than the recursive least square method for parameter estimation of the linear model. Optimal function discrimination was acquired when sampling frequency was 500Hz and two electrodes were attached to bicep and tricep muscle, respectively. Parameter values were independent of variance and the number of minimum data for function discrimination was 200. Bayesian discrimination method turned out to be a better one than parallel filtering method for functional discrimination recognition.

• 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.

• Journal of Mechanical Science and Technology
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• v.14 no.3
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• pp.338-349
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• 2000

Neck muscle forces and spinal loads at the C4/5 level were estimated that result from isometric voluntary ramp efforts gradually developing to maximums in flexion, extension, left lateral bending and right lateral bending. Electromyographic (EMG) activities, a three-dimensional anatomic data of the neck and a hybrid model, EMG-assisted optimization (EMGAO) model, were used. The model computed the cervical loads at 25%,50%,75%, and 100% of peak moments. The highest model-predicted C4/5 joint compressive forces occurred during flexion; $361\;({\pm}164)\;N,\;811\;({\pm}288)\;N,\;1207\;({\pm}491)\;N\;and\;1674\;({\pm}319)\;N$ in 25%, 50%, 75% and 100% of peak moment respectively. Variations in load distribution among the agonistic muscles and co-contractions of antagonistic muscles were estimated during ramp efforts. Results suggest that higher C4/5 joint loads than previously reported are possible during isometric, voluntary muscle contractions. These higher physiological loads at C4/5 level must be considered possible during orthopedic reconstruction at this level.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.395-401
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• 1996

In this paper a new neural network model, time delayed counterpropagation neural networks (TDCPN) which have high recognition rate and short total learning time, is proposed for electromyogram(EMG) recognition. Signals the proposed model increases the recognition rates after learned the regional temporal correlation of patterns using time delay properties in input layer, and decreases the learning time by using winner-takes-all learning rule. The ouotar learning rule is put at the output layer so that the input pattern is able to map a desired output. We test the performance of this model with EMG signals collected from a normal subject. Experimental results show that the recognition rates of the suggested model is better and the learning time is shorter than those of TDNN and CPN.