• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.65-68
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• 2001

In the rehabilitation robotic arm systems for the disabled with spinal code injury, EMG signals are used in the control of the robotic arm. EMG signals are corrupted by many kinds of noises such as ECG signal, power noise and contact noise of electrode. Noise rejection improves the performance of the EMG pattern classification. In this paper, a variable bandwidth filter (VBF) and wavelet transform are used for the noise rejection of EMG signals and the comparison of SNR is given. Also, some statistical characteristics of features are investigated.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.249-257
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• 2001

The voluntary contracted EMG signal of spinal cord injured patients is very small because the information from central nervous system is not sufficiently transmitted to $\alpha$ motor neuron or muscle fiber. Therefore the acquisited EMG signal from needle or surface electrodes can not be identified obvious voluntary contraction pattern by muscle movement. In this paper we propose the extraction technique of voluntary muscle contraction and relaxation pattern from EMG signal of spinal cord injured patient whose EMG signal is composed of the linear sum of mo색 unit action potentials with two noise sources, additive noise assumed to be white Gaussian noise and high frequency discharge assumed to be not motor unit action potential but impulsive noise. In order to eliminate impulsive noise and additive noise from voluntary contracted EMG signal, we use the FatBear filter which is a nonarithmetic piecewise constant filter, and multiscale nonlinear wavelet denoising processing, respectively. The proposed technique is applied to the EMG signal acquisited from transverse myelitis patients to extract voluntary muscle contraction pattern.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.285-288
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• 2008

In ubiquitous computing environment the biological signal ECG (Electrocardiogram signal) is usually recorded with noise components. Adaptive interference (or noise) canceller do adaptive filtering of the noise reference input to maximally match and subtract out noise or interference from the primary (signal plus noise) input thereby adaptively eliminate unwanted interference from the ECG signal. Measured Stress ECG (or exercise ECG signal) signal have three major noisy component like baseline wander noise, motion artifact noise and EMG (Electro-mayo-cardiogram) noise. These noises are not only distorted signal but also root of incorrect diagnosis while ECG data are analyzed. Motion artifact and EMG noises behave like wide band spectrum signals, and they considerably do overlapping with the ECG spectrum. Here the multidimensional adaptive method used for filtering which is more effective to improve signal to noise ratio.

• Biomedical Science Letters
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• v.24 no.1
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• pp.43-49
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• 2018

Electrocardiogram (ECG) is a widely used method to diagnose electrical activity of heart. Although it is a reliable and easy method, ECG could be interfered by electrical signals. One of the interfering signals is electromyogram (EMG) that is caused by muscle contraction in any parts of the body except heart. To avoid the EMG noise, an examinee is advised to be relaxed on supine position while measuring ECG. Sometimes, patients who can't put their arms and legs down on bed due to some reasons such as cast on arms or legs necessarily have the EMG noise. But detailed information about how much of the noise could be induced by positional change of arms and legs has not been reported. Here we examined the noise by analyzing ECG data from 14 candidates, 7 males and 7 females. The ECG data was obtained using the standard 12 lead ECG. EMG noise was induced by raising arms and legs at $90^{\circ}$, $60^{\circ}$ or $30^{\circ}$. Because arms are located close to the heart, noise by the raised arms was analyzed toward left or right arm separately. All of the examinees showed similar pattern of the EMG noise. EMG noise by positional change of left or right arm was clearly monitored in different limb leads. Change of leg positions induced the noise that was monitored in aVF of augmented leads and II and III of limb leads. There was a difference in degree of the noise between male and female examinees. In addition to the EMG noise, decrease of PR interval was monitored in particular positional changes, which was prominent in male examinees. These results will enlarge fundamental understanding about EMG noise in ECG.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.10
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• pp.934-939
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• 2011

This paper experimentally deals with the relationship between the ankle electromyogram(EMG) and walking motion in order to activate the ankle joint of a walking-assistance robot for rehabilitation. Based on the anatomical structure and motion pattern of an ankle joint, major muscles were selected for EMG measurements. Surface EMG signals were monitored for several human bodies at various stride distances and stride frequencies. Root-mean-squared magnitude of EMG signals were related with the walking conditions. It appeared that the magnitude of the ankle EMG signal was linearly proportional to the stride distance and stride frequency, and thus to the walking speed.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.6
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• pp.417-422
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• 2005

This paper presents an EMG pattern classification method to identify motion commands for the control of the artificial arm by SOM-TVC(self organizing map - tracking Voronoi cell) based on neural network with a feature parameter. The eigenvalue is extracted as a feature parameter from the EMG signals and Voronoi cells is used to define each pattern boundary in the pattern recognition space. And a TVC algorithm is designed to track the movement of the Voronoi cell varying as the condition of additive noise. Results are presented to support the efficiency of the proposed SOM-TVC algorithm for EMG pattern recognition and compared with the conventional EDM and BPNN methods.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.398-401
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• 1997

The EMG signal of spinal cord injured patient is very feeble because that the information from central nervous system is not sufficiently transmitted to molter neuron or muscle fiber. Therefore the observer can not observe contraction and relaxation movement of muscle from the raw EMG signal. In this paper, we propose the muscle contraction and relaxation pattern analysis method of spinal cord injured patient whose EMG signal is composed of the sum of motor unit action potential train with additive white Gaussian noise and impulsive noise. From the EMG model, we denoise impulsive noise using median filter which is a kind of nonlinear filter and the output of median filter is transformed to wavelet transform domain for denoising additive white Gaussian noise using threshold level removal technique. As a result, we can obtain the clear contraction and relaxation pattern.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.6
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• pp.302-308
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• 2001

SVD(Singular Value Decomposition) filter for the suppression of EMG in multi-lead stress ECG is studied. SVD filter consists of two parts. In the first part, the basis vectors were chosen from the averaged singular vectors obtained from the decomposed noise-free ECG. The singular vector is computed from the stress ECG and is compared itself with basis vectors to know whether the noise exist in stress ECG. In the second part, the existing elimination method is used, when one(or two) channels is(or are) contaminated by noise. But the proposed enhanced SVD filter is used in case of having the noise in the many channels. During signal decomposition and reconstruction, the noise-free channel or the least noisy channel have the weight of 1, the next less noisy channel has the weight of 0.8. In this way, every channel was weighted by decreased of 0.2 in proportion to the amount of the added noise. For the evaluation of the proposed enhanced SVD filter, we compared the SNR computed by the enhanced SVD filter with the standard average filter for the noise-free signal added with artificial noise and the patient data. The proposed SVD filter showed better in the SNR than the standard average filter. In conclusion, we could find that the enhanced SVD filter is more proper in processing multi-lead stress ECG.

• Science of Emotion and Sensibility
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• v.6 no.1
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• pp.27-32
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• 2003

The objectives of this study the effects of fabric noise from active wear on electrodiagnosis(EMG), to examine the effects on subjective sensation, and to relate the EMG results and the subjective sensation.. Five nylon water repellent taffeta fabrics were rubbed each other and the fabric noise were recorded. EMG was done from 10 female university students and electrodes were attached on each participant's upper arm and lower am. The subjective sensation was measured by FMME(Free Modulus Magnitude Estimation). The EMG values from upper arm showed higher voltage than those from lower arm, and the differences between values with fabric sound and without were larger at upper arm than those at lower am. EMG decreased when fabric sound was evaluated soft and pleasant, however It increased in proportion as fabric sound was evaluated loud and sharp. The predicted models for subjective sensation using physical sound properties and EMG results were well explained except roughness. Pleasantness was well predicted by EMG at upper am and EMG at lower arm, as the result, it was explained that the lower the EMG, the more pleasant the participant.

• 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.