• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.843-850
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• 2017

The EMG amplitude estimator, which has been investigated as an indicator of muscle force, is utilized as the control input to artificial prosthetic limbs. This paper describes an application of the optimal EMG amplitude estimator to the surface EMG signals recorded during constant isometric %MVC (maximum voluntary contraction) for 30 seconds and reports on assessing performance of the amplitude estimator from the application. Surface EMG signals, a total of 198 signals, were recorded from biceps brachii muscle over the range of 20-80%MVC isometric contraction. To examine the estimator performance, a SNR(signal-to-noise ratio) was computed from each amplitude estimate. The results of the study indicate that ARV(average rectified value) and RMS(root mean square) amplitude estimation with forth order whitening filter and 250[ms] moving average window length are optimal and showed the mean SNR improvement of about 50%, 40% and 20% for each 20%MVC, 50%MVC and 80%MVC surface EMG signals, respectively.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.105-114
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• 2008

The purpose of this study was to compare of muscles in clean and jerk techniques between 69kg class(n=3), 58kg class(n=3) in high school female weight lifters using EMG(electromyographic) system. EMG analysis were executed on 6 major muscles and dividing clean and jerk techniques into 6 phases. In that result, in the difference by weight, it was shown that EMG value increased gradually as the weight is raised of all muscles group & phases in 58kg class. In EMG signal scale by classes, it was shown that EMG signal scale didn't increase according to class & weight. In the result of this study, that EMG value was inconsistent in 69kg class is showing that the consideration of the technical factor together with muscle power has positive affect more on the performance improvement in the heavy class.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.2
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• pp.291-296
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• 2014

Most of the statistical signal analysis processed in the time domain and the frequency domain are based on the assumption that the signal is weakly stationary(wide sense stationary). Therefore, it is necessary to know whether the surface EMG signals processed in the statistical basis satisfy the condition of weak stationarity. The purpose of this study is to analyze the accuracy of the Run-test, modified Run-test, RA(reverse arrangement)-test, and modified RA-test for assessing surface EMG signal stationarity. Six stationary and three non-stationary signals were simulated by using sine wave, AR(autoregressive) modeling, and real surface EMG. The simulated signals were tested for stationarity using nine different methods of Run-test and RA-test. The results showed that the modified Run-test method2 (mRT2) classified exactly the surface EMG signals by stationarity with 100% accuracy. This finding indicates that the mRT2 may be the best way for assessing stationarity in surface EMG signals.

• Journal of the military operations research society of Korea
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• v.8 no.1
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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.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.493-494
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• 2013

본 논문에서는 컬-덤벨 운동의 연속, 반복적인 근 수축/이완 동작에 대한 전환 시점을 검출하기 위한 EMG(Electromyogram) 신호 분석 모듈을 설계하였으며 이에 대한 안드로이드 플랫폼 기반의 테스트 프로그램을 구현하였다. 제안하는 모듈로는 무산소 운동 분석에의 활용도가 높은 EMG 신호를 기반으로 근 수축/이완의 전환 시점을 95% 검출하며, 이를 개선 및 확장함으로써 현재 상용화 및 보급화된 운동 가이드 시스템에서 제공할 수 없었던 무산소 운동의 체계적 가이드를 제공할 수 있을 것으로 기대된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.631-632
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• 2013

본 논문에서는 EMG 신호를 기반으로 컬-덤벨 운동의 운동량을 분석하고 각 근육별 최적 운동량을 정의하여 운동을 가이드(guide)하는 방법을 제안한다. 제안하는 방법으로 운동 체육학 분야에서의 EMG 신호 분석 및 활용 범위를 넓힐 수 있으며, 사용자는 컬-덤벨 운동 중 각 근육별 운동량 및 이에 대한 적절성을 안내받아 올바른 자세를 유지함으로써 컬-덤벨의 운동 효과를 극대화할 수 있다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.114-120
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• 2018

In this paper we implemented an EMG sensor system for wearable devices to obtain and analyze of EMG signals. The performance of the implemented sensor system is evaluated by the correlation analysis of muscle fatigue and muscle activation to clinical EMG system and compared with power consumption of the measured power of our system and commercial systems. In experiments with biceps and triceps brachii of 5 objects, The correlation values of muscle fatigue and muscle activation between our system and the clinical EMG system is 1.1~1.4 and about 1.0, respectively. And also the power consumption of our system is 25~50% less than that of some commercial EMG sensor systems.

• Korean Journal of Occupational Health Nursing
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• v.8 no.2
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• pp.156-161
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• 1999

The EMG(Electromyography) analysis was used to identify the fact the degree of inclined step was selected as dependent variable and feet muscle fatigue was selected as a independent variable. In a final result from EMG test. the shift in median frequency (MF) with 20, 25, 30, 35 degree of inclined steps indicated that 30 degree step was identified as most effective for a decrease in feet muscle fatigue. In a department store, 80% of the workers are female standing sales workers. They work at standing on average 10 hours per day. They performed heavy duty jobs such as lifting, lowering. packing and carrying heavy materials. Furthermore, even though they have work shoes, they usually use various kind of high heels. Eventually, this situation develops low-back-pain (LBP) problems for female workers. In conclusion, it is recommended that a particular branch in a department store claimed this step can effectively to circulate blood and significantly decrease feet muscle fatigue in lower extremity.

• Proceedings of the KIEE Conference
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• 2000.11d
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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.

• The Journal of Korean Institute for Practical Engineering Education
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• v.3 no.1
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• pp.200-205
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• 2011

This research is first to analyze an electomygram reaction of the lower limbs among the muscles used during a back-round kicking of Taekwondo, second to analyze the relationship between the muscles of the kicking limb and those of the supporting limb, third to compare EMG activity of expert with novice. Measured muscles are as follows; rectus fermoris which control flexion and stretching of femora and knee joints, vastus medialis and vastus lateralis, which control flexion and stretching of legs, and biceps fermoris, which control stretching of femora, bending of knee joints and abduction of the crus. The electomygram reaction of all these selected muscles were analyzed. Three kicks were performed, and electomygram of each of kicks were measured. Then, electomygram reaction of the best kick was used for electomygram analysis.