• 대한의용생체공학회:의공학회지
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• 제27권5호
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• pp.237-244
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• 2006

This study proposes an approach to the performance improvement of EMG(Electromyogram) pattern recognition. MFCC(Mel-Frequency Cepstral Coefficients)'s approach is molded after the characteristics of the human hearing organ. While it supplies the most typical feature in frequency domain, it should be reorganized to detect the features in EMG signal. And the dynamic aspects of EMG are important for a task, such as a continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most approaches. Thus, this paper proposes reorganized MFCC and HMM-GMM, which is adaptable for the dynamic features of the signal. Moreover, it requires an analysis on the most suitable system setting fur EMG pattern recognition. To meet the requirement, this study balanced the recognition-rate against the error-rates produced by the various settings when loaming based on the EMG data for each motion.

• 전기학회논문지
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• 제62권7호
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• pp.1014-1019
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• 2013

Signal processing techniques based on fractional order calculus have been successfully applied in analyzing heavy-tailed non-Gaussian signals. It was found that the surface EMG signals from the muscles having nuero-muscular disease are best modeled by using the heavy-tailed non-gaussian random processes. In this regard, this paper describes an application of digital fractional order lowpass differentiators(FOLPD, weighted FOLPD) based on the fractional order calculus in detecting peaks of surface EMG signal. The performances of the FOLPD and WFOLPD are analyzed based on different filter length and varying MUAP wave shape from recorded and simulated surface EMG signals. As a results, the WFOLPD showed better SNR improving factors than the existing WLPD and to be more robust under the various surface EMG signals.

• 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.

• 전기학회논문지
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• 제66권5호
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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.

• 대한의용생체공학회:의공학회지
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• 제29권6호
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• pp.502-511
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• 2008

This study was designed to develop a new algorithm to extract the voluntary EMG and the evoked EMG from a mixed EMG generated when the muscle is stimulated both voluntarily and by electrical stimulation in the FES system. The proposed parallel filter algorithm consists of three phases: (1) Fourier transform of the mixed EMG, (2) multiplication of the transformed signal to two frequency functions, and (3) inverse Fourier transform. Four incomplete spinal cord injured patients participated in the experiments to evaluate the algorithm by measuring the knee extensor torque and the EMG signals from the quadriceps. Two functions of the algorithms were evaluated: (1) extraction of the evoked EMG and (2) the voluntary EMG from the mixed EMG. The results showed that the algorithm enabled us to separate the two EMG components in real time from the mixed EMG. The algorithm can and will be used for estimating the voluntary torque and the evoked torque independently through an artificial neural network based on the two EMG components, and also for generating a trigger signal to control the on/off time of the FES system.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1991년도 추계학술대회
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• pp.25-29
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• 1991

In the field of prosthesis arm control, tile pattern classification of the EMG signal is a required basis process and also the estimation of force from col looted EMG data is another necessary duty. But unfortunately, what we've got is not real force but an EMG signal which contains the information of force. This is the reason why he estimate the force from the EMG data. In this paper, when we handle the EMG signal to estimate the force, spatial prewhitening process is applied from which the spatial correlation between the channels are removed. And after the orthogonal transformation, which is used in the force estimation process the transformed signal is inputed into the probabilistic model for pattern classification. To verify the different results of the multiple channels, SNR(signal to noire ratio) function is introduced.

• 전기전자학회논문지
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• 제22권4호
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• pp.1195-1201
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• 2018

본 논문은 인간의 신체활동에 있어서 뇌의 신호가 연결된 근육으로 정보를 제공하고 받아오는 과정에서 유의미한 결과를 나타내는지에 관한 관계를 해석한다. 사람의 의식적 활동은 활동에 필요한 근육의 동작을 위하여 뇌로부터 생성된 전기신호의 전달에 의해 가능해 진다. 근육의 활성정보를 가지고 있는 근전도 신호는 근육활성화의 결과를 전기적인 신호로 출력하는데, 이 출력은 보통 근육의 수축과 이완에 따른 근육활성 정보를 출력한다. 본 연구에서는 이런 뇌전도와 근전도를 실시간으로 추출하여 데이터를 획득하고, 데이터 분석을 통해 눈으로 쉽게 확인하기 어려운 두 신호간의 관계를 분석하는데 목적이 있다.

• 한국운동역학회지
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• 제16권4호
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• pp.1-12
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• 2006

This research was to know EMG tendency on increasing record in snatch weightlifting. In order to perform this research, we choiced 3 man national weightlifters, EMG analysis were executed on 8 major muscle(Latissimus Dorsi, Trapezius, Anterior Deltoid, Posterior Deltoid, Gastrocenemius, Vastus Medialis, Erector spinae, Abdominal). First trial record of athletics is 80% of each maximal record and increase the $5{\sim}10kg$ gradually. In this study, EMG signal scale of all muscle except posterior Deltoid muscle don't increased according to increasing the barbell weight, This showed a difference between general recognition and experiment result. In posterior Deltoid muscle, EMG signal scale increased according to increasing the barbell weight. It was assumed that EMG signal of protagonist shows possibility of linear increasing if motion have a consistency. It was assumed that In present, In order to increase one's record to $5{\sim}10kg$, Motion consistency training is more effective training method than increasing the muscle force.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2008년도 추계종합학술대회 B
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• pp.161-164
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• 2008

본 논문은 근전도 신호를 검출하고 로봇팔의 수직제어시스템을 구현하기 위한 연구이다. EMG 신호는 사람의 팔 근육에 흐르는 미세한 생체신호가 표면전극센서에 의해서 사용하여 검출되고 고성능 증폭, 필터링, ADC과 로봇팔의 서보 모터 구동 시스템으로 구현된다. 실험은 팔근육 움직임에 따른 EMG신호와 로봇팔의 다단계 수직제어 각도를 모니터링 한다. 시스템의 실험결과 수직제어각도는 2도 정도이며 평균오차는 5%이다.

• 한국지능시스템학회논문지
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• 제26권3호
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• pp.176-181
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• 2016

본 논문은 EMG(Electromyogram) 신호 기반의 웨어러블 기기를 이용하여 화재 감지 자율 주행 로봇을 제어하는 시스템을 제안하였다. 사용자의 EMG 신호를 읽어내기 위한 기기로는 Myo armband를 이용하였다. EMG 신호의 데이터를 블루투스 통신을 이용하여 컴퓨터로 전송한 후 동작을 분류하였다. 그 후 다시 블루투스를 이용하여 분류한 데이터 값을 uBrain 로봇으로 전송해 로봇이 움직일 수 있도록 구현하였다. 로봇을 조종 가능한 명령으로는 직진, 우회전, 좌회전, 정지를 구성하였다. 또한 로봇이 사용자로부터의 블루투스 신호를 받아오지 못하거나 사용자가 주행모드 변경의 명령을 내리면 로봇이 자율 주행을 하도록 하였다. 로봇이 주변을 돌아다니면서 적외선 센서로 화재를 감지하면 LED를 깜빡여 로봇 주변의 상황을 확인할 수 있도록 하였다.