• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.1459-1460
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• 2013

• Journal of Oral Medicine and Pain
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• 제13권1호
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• pp.23-33
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• 1988

The purpose of this study was to standardized and classify the coordination pattern among the chewing side and non-chewing side masseter and anterior muscles, in terms of EMG values on lateral excursion, clenching, and mastication in presence of the non-chewing side. In this study, 25 subjects were selected for experiment of lateral excursion, clenching and mastication and EMG value of the masseter and anterior temporal muscle on both sides were recored 2 times respectively. The bioelectric processor model EM2(Myo-tonic research, INC. U.S.A.) with the surface electrodes were used to record the EMG activity during all experimental procedures. The results were as follows : 1. During lateral excursion on intereference of non-chewing side, the EMG values of the temporal muscle were significantly more prominent than those of the temporal muscle on the non-chewing side. The EMG values of non-chewing side were significantly more prominent than those of chewing side on the both side masseter muscle and those of chewing side were significantly more prominent than those of non-chewing side on the both side temporal muscle. 2. During clenching on the occlusal interferance, the EMG values of non-chewing side masseter muscle were most prominent. 3. During mastication on the occlusal interferance, the EMG values of the chewing side temporal muscle were most prominent and those of non-chewing side temporal muscle were the lowest. 4. The EMG values of temporal muscle of non-chewing side on interferance were significantly more prominent than those of canine guidance during lateral excursion. 5. During clenching on the occlusal interferance, the EMG values of the masseter and the temporal muscles of the non-chewing side were significantly more prominent than canine guidance, but those of chewing side temporal muscle on canine guidance were significantly more prominent than those of interferance. 6. During mastication on canine guidance, the EMG Values of the temporal muscle on the chewing side, the masseter muscle on the chewing side and the temporal muscle on the non-chewing side were more prominent than those of interferance, but temporal muscle of non-chewing side was not different between canine guidance and occlusal interferance on non-chewing side.

• 한국의류산업학회지
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• 제18권6호
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• pp.755-765
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• 2016

Recently, interest in monitoring health and sports is growing because of the emphasis on wellness, which is accelerating the development and commercialization of smart clothing for biosignal monitoring. In addition to exerciseeffect monitoring clothing that tracks heart rate and respiration, recently developed clothing makes it possible to monitor muscle balance using electromyogram (EMG). The electrode for EMG have to attached to an accurate location in order to obtain high-quality signals in surface EMG measurement. Therefore, this study develops monitoring clothing suitable for different types of human bodies and aims to extract suitable range of EMG according to movements in order to develop self-fitness monitoring clothing based on EMG measurement. This study identified and attached electrodes on six upper muscles and two lower muscles of ten males in their 20s. After selecting six main motions that create a load on muscles, the 8-ch wireless EMG system was used to measure amplitude value, noise, SNR and SNR (dB) in each part and statistical analysis was conducted using SPSS 20.0. As a result, the suitable range for EMG measurement to apply to clothing was identified as four parts in musculus pectoralis major; three parts in muscle rectus abdominis, two parts each in shoulder muscles, backbone erector, biceps brachii, triceps brachii, and musculus biceps femoris; and four part in quadriceps muscle of thigh. This was depicted diagrammatically on clothing, and the EMG-monitoring sensing locations were presented for development of self-fitness monitoring.

• 공학교육연구
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• 제19권1호
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• pp.31-36
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• 2016

These days, ICT-related products are pouring out due to development of mobile technology and increase of smart phones. Among the ICT-related products, wearable devices are being spotlighted with the advent of hyper-connected society. In this paper, a body-attached type wearable device using EMG(electromyography) sensors is studied. The research field of EMG sensors is divided into two parts. One is medical area and another is control device area. This study corresponds to the latter that is a method of transmitting user's manipulation intention to robots, games or computers through the measurement of EMG. We used commercial device MYO developed by Thalmic Labs in Canada and matched up EMG of arm muscles with gesture controller. In the experiment part, first of all, various arm motions for controlling devices are defined. Finally, we drew several distinguishing kinds of motions through analysis of the EMG signals and substituted a joystick with the motions.

• 대한임상검사과학회지
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• 제46권1호
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• pp.38-45
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• 2014

Intraoperative Neurophysiological Monitoring (INM) inspection has a very important role. While preserving the patient's neurological function be sure to safe surgery, neurological examination should thank. Cerebello pontine angle tumor surgery, especially in the nervous system is more important to the meaning of INM. In cochlear nerve, facial nerve, trigeminal nerve, which are intricate brain surgery, doctors are only human eye and brain to the brain that it is virtually impossible to distinguish the nervous system. They receives a lot of help from INM. In this paper, we examined six kinds broadly. First, the methods of spontaneous EMG and Free-running EMG, which can instantly detect a damage inflicted on a nerve during surgery. Second, methods of triggered EMG and direct nerve electrical stimulation, which directly stimulate a nerve using electricity to distinguish between nerves and brain tumors. Third, the method of knowing a more accurate neurologic status by informing neurological surgeons about Free-running EMG wave forms that are segmetalized into four. Fourth, three ways of knowing when a patient will be awaken from intraoperative anesthesia, which happens due to a weak anesthetic. Fifth, a method of understanding the structures of a brain tumor and a facial nerve as five dividend segments. Sixth, comparisons between cases normal facial nerve recovery and occurrence of a facial nerve paralysis during the postoperative course.

• Safety and Health at Work
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• 제3권1호
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• pp.31-42
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• 2012

Objectives: The objectives of this study were to determine the psychological fatigue and analyze muscle activity of production workers who are performing processes jobs while standing for prolonged time periods. Methods: The psychological fatigue experienced by the workers was obtained through questionnaire surveys. Meanwhile, muscle activity has been analyzed using surface electromyography (sEMG) measurement. Lower extremities muscles include: erector spinae, tibialis anterior, and gastrocnemius were concurrently measured for more than five hours of standing. Twenty male production workers in a metal stamping company participated as subjects in this study. The subjects were required to undergo questionnaire surveys and sEMG measurement. Results: Results of the questionnaire surveys found that all subjects experienced psychological fatigue due to prolonged standing jobs. Similarly, muscle fatigue has been identified through sEMG measurement. Based on the non-parametric statistical test using the Spearman's rank order correlation, the left erector spinae obtained a moderate positive correlation and statistically significant ($r_s$ = 0.552, p < 0.05) between the results of questionnaire surveys and sEMG measurement. Conclusion: Based on this study, the authors concluded that prolonged standing was contributed to psychological fatigue and to muscle fatigue among the production workers.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.55-58
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• 1997

In this paper, we study a signal processing method which extracts each MUAP(motor unit action potential) from EMG(Electromyogram) interference pattern or clinical diagnostic purposes. First of all, differential digital filtering is selected or eliminating the spike components of the MUAP's from the background noise. And, the algorithm identifies the spikes over the certanin threshold by template matching in frequency domain. After missing or false firing actor is cut off at the IPI(inter pulse interval) histogram, we averages the MUAP waveforms from the raw signal using the identified spikes as triggers, and Finally, measures their amplitudes, durations, and numbers of phases. Specially, We introduce algorithm performed by template matching in the frequency domain. A typical 3-s signal recorded from the biceps brachii muscle using a conventional needle electrode during a isometric contraction is used. Finally, the method decomposed five simultaneous active MUAP's from original EMG signal.

• 대한의용생체공학회:의공학회지
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• 제31권4호
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• pp.310-315
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• 2010

Estimation of muscle forces is important in biomechanics, therefore many researchers have tried to build a muscle model. Recently, optimization techniques for adjusting muscle parameters, i.e. EMG-driven model, have been used to estimate muscle forces and predict joint moments. In this study, an EMG-driven model based on the previous studies has been developed and isometric and isokinetic contraction movements were evaluated to validate the developed model. One healthy male participated in this study. The dynamometer tasks were performed for maximum voluntary isometric contractions (MVIC) for ankle dorsi/plantarflexors, isokinetic contraction at both $30^{\circ}/s$ and $60^{\circ}/s$. EMGs were recorded from the tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis and soleus muscles at the sampling rate of 1000 Hz. The MVIC trial was used to customize the EMG-driven model to the specific subject. Once the subject's own model was developed, the model was used to predict the ankle joint moment for the other two dynamic movements. When no optimization was applied to characterize the muscle parameters, weak correlations were observed between the model prediction and the measured joint moment with large RMS error over 100% (r = 0.468 (123%) and r = 0.060 (159%) in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). However, once optimization was applied to adjust the muscle parameters, the predicted joint moment was highly similar to the measured joint moment with relatively small RMS error below 40% (r = 0.955 (21%) and r = 0.819 (36%) and in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). We expect that our EMG-driven model will be employed in our future efforts to estimate muscle forces of the elderly.

• 한국국방경영분석학회지
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• 제8권1호
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• pp.77-98
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• 1982

Experiments have been performed for estimating the individual muscle capabilities of the biceps brachii and the quadriceps femoris muscle. The surface EMG has been recorded on the bellies of the biceps brachii and the quadriceps femoris muscle during isometric contractions at $50\%,\;75\%,\;and\;100\%$ MVC. The rectified EMG amplitudes of the maximum voluntary contraction (MVC) were in the range of $2.8\~3.0\;mV\;and\;6.9\~7.2\;mV$ the biceps brachii and the quadriceps femoris, respectively. In the biceps brachii, Type S motor units were recruited in the range of $41\~49\%$ MVC; and Type F motor units were recruited in the range of $51\~59\%$ MVC, In the quadriceps femoris, Type S, Type SF, and Type F motor units were recruited in the ranges of $31\~38\%,\;33\~48\%$, and $21\~29\%$ MVC respectively.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2209-2212
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• 2003

In this paper, we propose the method to extract the important frequency bands from the EMG signal, and for generation of feature vector using the important frequency bands. The EMG signal is measured with 4 sensor and is recorded as 4 channel’s time series data. The same frequency bands from 4 channel’s frequency components are selected as the important frequency bands. The feature vector is calculated by the function formed using the combination of selected same important frequency bands. The EMG signals acquired from seven wrist motion type are recognized by changing into the feature vector formed. Then, the extraction and generation is performed by using the double combination of the genetic algorithm (GA) and the neural network (NN). Finally, in order to illustrate the effectiveness of the proposed method, computer simulations are done.