• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.115-115
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• 2000

A gait pattern classification method using electromyography(EMG) signal is presented. The gait pattern with four stages such as stance, heel-off, swing and heel-strike is analyzed and classified using feature parameters such as zero-crossing, integral absolute value and variance of the EMG signal. The EMG signal from Tibialis Anterior and Gastrocnemius muscles was obtained using the surface electrodes, and low-pass filtered at 10kHz. The filtered analog signal was sampled at every 0.5msec and converted to digital signal with 12-bit resolution. The obtained data is analyzed and classified in terms of feature parameters. Analysis results are given to show that the gait patterns classified by the proposed method are feasible.

• 대한의용생체공학회:의공학회지
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• 제13권1호
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• pp.1-8
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• 1992

In the field of prosthesis arm control, the pattern classification of the EMG signal is a required basis process and also the estimation of force from collected 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 we 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 noise ratio) function is introduced.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 V
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• pp.2737-2740
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• 2003

This paper has been studied an algorithm for EMG signal amplitude estimation in noisy environment. The proposed method has the first stage decomposing the row vector from the delayed EMG signal and the second stage computing the eigenvalues by the eigen decomposition from the covariance matrix of the EMG signal matrix. The last stage is the estimation of RMS values from the eigenvalues. The proposed method was effective when the amplitude of the EMG signal is small, which means the signal to noise ratio is low.

• 대한의용생체공학회:의공학회지
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• 제22권3호
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• pp.249-257
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• 2001

본 논물에서는 척수 손상으로 인하여 암이 수축 및 이완시 미약한 근전도, 신호를 발생시키는 환자로부터 명확한 수축 및 이완 패턴을 추출하기 위한 신호 처리 기법을 제안하였다. 제안한 방법은 비선형 고정 필터의 일종인 FatBear 필터를 이용하여 거대 운동단위 활동전위로 의심되는 충격 잡음을 제거하고 웨이브렛 평면에서 비선형 멀티 스케일 필터링 기법을 이용하여 가산 잡음을 제거하는 것으로서 횡단성 척수염으로 인한 마미 증후근을 보이는 환자들에게 적용하여 명확한 수축 및 이완 패턴을 추출할 수 있었다.

• 대한전자공학회논문지
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• 제24권2호
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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.

• 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.

• 대한의용생체공학회:의공학회지
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• 제15권3호
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• pp.305-316
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• 1994

All physical data in the real world are nonstationary signals that have the time varying statistical characteristics. Although few algorithms suitable to process the nonstationary signals have ever been suggested, these are treated the nonstationary signals under the assumption that the nonstationary signal is a piece-wise stationary signal. Recently, statistical analysis algorithms for the nonstationary signal have concentrated so much interest. In this paper, nonstationary EMG signals are mapped onto the orthogonal wavelet transform domain so that the eigenvalue spread of its autocorrelation matrix could be more smaller than that in the time domain. Then the model in the wavelet transform domain and an algorithm to estimate the model parameters are suggested. Also, an test signal generated by a white gaussian noise and the EMG signal are identified, and the algorithm performance is considered in the sense of the mean square error and the evaluation parameters.

• 제어로봇시스템학회논문지
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• 제14권12호
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• pp.1278-1285
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• 2008

An optimization-based real-time joint angle extraction method of human elbow is proposed by processing the biomedical signal of surface EMG (electromyogram) measured at the center point of biceps brachii. The EMG signal is known as non-stationary (time-varying) signal, but we assume that it is quasi-stationary because a physical or physiological system has limitations in the rate at which it can change its characteristics. Based on the assumption, a pre-processing method to obtain pre-angle values from raw EMG signal is firstly suggested, and then an optimization method to minimize the error between the pre-angle and real joint angle is proposed in this paper. Finally, we suggest the experimental results showing the effectiveness of the proposed algorithm.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권11호
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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.

• 대한의용생체공학회:의공학회지
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• 제11권2호
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• pp.249-256
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• 1990

In this study, LPC cepstrum coefficients are used as feature vector extracted from AR model of EMG signal, and a reduced-connection network whlch has reduced connection between nodes is constructed to classify and recognize EMG functional classes. The proposed network reduces learning time and improves system stability Therefore it is Ehown that the proposed network is appropriate in recognizing function of EMG signal.