• Journal of Biomedical Engineering Research
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• v.13 no.1
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.124-130
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• 2018

The EMG amplitude estimator, which has been investigated as an indicator of muscle force, is of high relevance not only in biomechanical studies but also more and more in clinical applications. This paper presents a new approach to estimate surface EMG amplitude by using the mean spike and mean turn amplitude(MSA and MTA) variables. Surface EMG signals, a total of 198 signals, were recorded from biceps brachii muscle over the range of 20-80%MVC isometric contraction and performance of the MSA and MTA variables applied to amplitude estimation of the EMG signals were investigated. To examine the performance, a SNR(signal-to-noise ratio) was computed from each amplitude estimate. The results of the study indicate that MSA and MTA amplitude estimations with first order whitening filter and 300[ms]-350[ms] moving average window length are optimal and show better performance(mean SNR improvement of 6%-15%) than the most frequently used variables(ARV and RMS).

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.889-890
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• 2006

This paper pertains to introducing the design of adaptive filters for the cancellation of muscle noise among several types of noise sources from the ECG signal. We used EMG signals measured along with ECG at the same time to use it as the reference input to the adaptive filter for the experiments. PSD results showed that the statistical characteristics of ECG are closely correlated with those of EMG.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2733-2736
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• 2003

This paper proposes a dry-type active surface EMG electrode for the myoelectric prosthetic hand. The designed electrode is small size for embedding in the socket of prosthetic hand, and it has three leads including the reference of signal. To acquire EMG signal rejected the power noise, a precision differential amplifier and various filters such as the band pass filter band rejection filter, low pass and high pass filter are embedded on the electrode. The final output of the electrode is integrated absolute EMG (IEMG) obtained by full rectifier and moving average circuits. From experimental results using the implemented dry-type active surface EMG electrode, the proposed electrode is feasible for the myoelectric prosthetic hand.

• Proceedings of the IEEK Conference
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• 2003.07c
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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.

• Journal of Biomedical Engineering Research
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• v.5 no.2
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• pp.133-142
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• 1984

The EMG signal can be considered as a signal source that expresses the intention of man because it is a electrical signal generated when the man contracts muscles. For proportional control of prostheses, the control signal proportional to the mousle contraction level must be estimated. Typically a foul-wave rectifier and low-pass filter are used to estimate the proportional control signal from the EMG signal. In this paper, it is proposed to use a logarithmic transformation and a linear minimum mean square error estimator. A logarithmic transformation maps the myoelectric signal into an additive control signal-plus-noise domain and the Kalman filter is used to estimate the control signal as a linear minimum mean square error estimator. The performance of this estimator is verified by the computer simulation and the estimator is applied to the EMG obtained from the biceps brachii muscle of normal subjects.

• Fashion & Textile Research Journal
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• v.18 no.6
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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.

• Korean Journal of Applied Biomechanics
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• v.16 no.2
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• pp.55-63
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• 2006

The purpose of this study was to investigate the effects of vibration exercise using surface electromyography. Seven male collegiate wrestlers were participated in this study. Each subject stood on the platform and the vibration was induced for 1min. WEMG8 EMG system was used to record muscle activity from Vastus lateralis, Biceps Femoris, Tibialis Anterior, and Gastrocnemius. The EMG data were sampled for 30 sec. during non-vibration and vibration half squat position, respectively. The raw data were band pass filtered to remove noise and full wave rectified Paired sample t-test were performed to see the differences of maximum and average EMG between non-vibration and vibration trials. The results indicated that vibration produced much more muscle contraction than that of non-vibration trial for all selected muscles even though the significant difference was found only from Biceps Femoris. This phenomenon was due to the individual differences so care must be taken to evaluate vibration intensity and position before personal training.

• Journal of Biomedical Engineering Research
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• v.15 no.3
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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.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.265-271
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• 2018

The recent prosthetic technologies pursue to control multi-DOFs (degrees-of-freedom) hand and wrist. However, challenges such as high cost, wear-ability, and motion intent recognition for feedback control still remain for the use in daily living activities. The paper proposes a multi-channel knit band sensor to worn easily for surface EMG-based prosthetic control. The knitted electrodes were fabricated with conductive yarn, and the band except the electrodes are knitted using non-conductive yarn which has moisture wicking property. Two types of the knit bands are fabricated such as sixteen-electrodes for eight-channels and thirty-two electrodes for sixteen-channels. In order to substantiate the performance of the biopotential signal acquisition, several experiments are conducted. Signal to noise ratio (SNR) value of the knit band sensor was 18.48 dB. According to various forearm motions including hand and wrist, sixteen-channels EMG signals could be clearly distinguishable. In addition, the pattern recognition performance to control myoelectric prosthesis was verified in that overall classification accuracy of the RMS (root mean squares) filtered EMG signals (97.84%) was higher than that of the raw EMG signals (87.06%).