• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.769-771
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• 2000

In this paper, pattern recognition scheme for EMG signal using artificial neural network is proposed. For manipulating ability, the movements of human arm are classified into several categories EMG signals of appropriate muscles are collected during arm movement. Patterns of EMG signals of each movement are recognized as follows: 1) The features of each EMG signal are extracted. 2) With these features, the neural network is trained by using feedforward error back-propagation (FFEBP) algorithm. The results show that the arm movements can be classified with EMG signals at high accuracy.

• 대한후두음성언어의학회지
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• 제10권1호
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• pp.24-29
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• 1999

Extrinsic laryngeal muscles are well known to be important for the classical singers. We tried to elucidate any differences in the function of above muscles between trained and untrained singers by non-invasive surface electromyography(EMG). Four trained sopranos and four untrained singers sang vowel /i/ at different pitch(E3, G3, C4, E4, G4, C5, E5, G5, C6). The EMG activities of the suprahyoid, infrahyoid and omohyoid muscles were measured using surface electrodes. In trained singers, infrahyoid muscle activities increased more than those of suprahyoid in most of pitch. To the contrary, in untrained singers, the pattern of EMG activities were variable among each subjects and the EMG activities of suprahyoid muscles were relatively greater than those of infrahyoid.

• 전자공학회논문지SC
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• 제38권4호
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• pp.31-36
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• 2001

본 논문에서는 근육의 움직임에 의해 유발되는 전기적 선호인 근전도(EMG) 신호를 신경회로망을 통해 분류하여 인체의 움직임을 파악하는 방법을 제안한다. 신호분류를 위한 신경회로망으로 학습에 의해 스스로 출력뉴런을 구성하는 SOM을 사용하였으며， 기존의 방식과 다르게 전처리 과정 없이 신호자세를 SOM의 입력으로 사용하여 패턴을 분류하는 간단한 방식이다. 실험과 시뮬레이션을 통해 제안한 방식의 유용성을 확인하였다.

• 로봇학회논문지
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• 제3권1호
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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.

• 대한의용생체공학회:의공학회지
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• 제27권3호
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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.

• 제어로봇시스템학회논문지
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• 제17권10호
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• pp.1014-1020
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• 2011

This presents a motion and force estimation system of human fingers by using an Electromyography (EMG) sensor module and a data glove system to be proposed in this paper. Both EMG sensor module and data glove system are developed in such a way to minimize the number of hardware filters in acquiring the signals as well as to reduce their sizes for the wearable. Since the onset of EMG precedes the onset of actual finger movement by dozens to hundreds milliseconds, we show that it is possible to predict the pattern of finger movement before actual movement by using the suggested system. Also, we are to suggest how to estimate the grasping force of hand based on the relationship between RMS taken EMG signal and the applied load. Finally we show the effectiveness of the suggested estimation system through several experiments.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권2호
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• pp.103-110
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• 2004

This paper proposes a new method on the EMG AR(autoregressive) modeling in pattern recognition for various head motions. The proper electrode placement in applying AR or cepstral coefficients for EMG signature discrimination is investigated. EMG signals are measured for different 10 motions with two electrode arrangements simultaneously. Electrode pairs are located separately on dominant muscles(S-type arrangement), because the bandwidth of signals obtained from S-type placement is wider than that from C-type(closely in the region between muscles). From the result of EMG pattern recognition test, the proposed mIAR(modified integrated mean autoregressive model) technique improves the recognitions rate around 17-21％ compared with other the AR and cepstral methods.

• 재활복지공학회논문지
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• 제7권2호
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• pp.19-26
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• 2013

본 논문에서는 다수의 특징 값 중에서 적합한 특징 및 채널을 선택하는 sEMG 신호기반 보행단계 인식기법을 제안한다. 제안하는 방법은 sEMG 신호 기반 분류기를 이용하여 하지 절단 환자의 동력의족을 제어하며, 적응적으로 특징 및 채널들을 선택하여 임베디드 시스템의 신호처리과정에서 발생하는 오버헤드를 감소시킨다. 또한 피험자의 보행 습관에 따라 근육 발달도가 다르다는 특성을 이용하여 피험자의 보행단계에 따라 사용 빈도가 높은 근육과 특징 추출 알고리즘을 선택함으로서 정확도를 향상시킨다. 실험 결과 피험자마다 인식율이 높은 근육이 다르다는 것을 발견하였다. 또한 모든 특징들과 채널들을 이용하는 기존 방법의 경우 50%의 평균정확도를 보인 반면에 제안한 방법은 91%의 평균정확도를 보였다. 따라서 소수의 발달된 근육과 이에 맞는 특징을 사용한 sEMG기반 보행단계인식 방법이 하지절단환자의 동력의족을 제어하는 데 적용될 수 있음을 확인하였다.

• 한국산업보건학회지
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• 제15권1호
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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.

• Journal of Oral Medicine and Pain
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• 제20권1호
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• pp.171-183
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• 1995

This study was carried out to investigate the relationship between tensed mandibular positions, muscle tenderness and EMG activity, respectively, and between range of motion of the neck and sternocleidomastoid muscle tenderness. Under stressful conditions, most of people take several types of behavioral patterns. Two of them observed frequently are clenching of teeth and grasping of fist. Prolonged clenching or grasping should increase electromyographic activity of associated muscle, especially muscles of mastication and neck muscles and will cause hyperfunction, dysfunction and muscle pain. So it is necessary to relate EMG activity with muscle pain. The author performed routine clinical examination in 47 patients with Temporomandibular Disorders, especially for presence or absence of muscle tenderness. Mandibular rest position was used as a baseline reference position and two more position in which EMG activity was taken were rest postion with grasping of fist and teeth clenching position. BioEMG of Biopak system (Bioresearch Inc, USA) was used for measuring of integrated EMG in masseter, anterior temporalis, anterior belly of digastic muscle and sternocleidomastoid muscle. To measure of the range of neck motion. CROM(Cervical-Range-of Motion, USA) was used. The obtained results were as follows : 1. EMG activity of all muscles except in masseter was higher in grasping of fist than those in rest position and there were significant correlation in EMG activity between the two position except in anterior belly of digastric muscle. 2. When comparing EMG activity between tender and non-tender muscle, all examined muscles did not show any significant difference. From this data, we could conclude that EMG activity was generally not changed with tenderness, of couse, it might be dependent with degree of muscle tenderness. 3. Number of tender points in examined muscles was also not significantly different between in patients with masticatory muscle disorders and in patients with internal derangement. 4. Cervical posture and range of motion of the neck was not differed significantly between in patients with and in patients without tenderness of sternocleidomastoid muscle.