• Journal of Biomedical Engineering Research
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• v.17 no.2
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• pp.155-164
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• 1996

In the clinical environment, measurements of some characteristics of the skeletal muscle are currently used to assess the severity of a neuromuscular disease or in some cases to assist in making a diagnosis. But a quantitative method of evaluation has not yet been introduced satisfactorily. In this paper, the skeletal EMG(biceps muscle, masseter muscle) analysis has been processed both in the time and in the frequency domain by designing the digital signal processing system based on pentium PC and transputer (IMS 7805). The experiment have been performed in five normal subjects, and various parameters have been statistically tested and compare4 As a results, the effective parameters obtained for the evaluation of skeletal EMG electrical activity are turn analysis, MiTi, MiTa, IEMG, PDF in the time domain, and are mean frequency, median frequency, skewness, kurtosis, muscle fatigue slope in the frequency domain. The designed H/W and S/W in this study can be used effectively for the establishment of EMG data base and for clinical research.

• 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.

• Journal of Biomedical Engineering Research
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• v.32 no.4
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• pp.328-335
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• 2011

For human factor engineering and wearable robot design, the quantitative assessment of physical workload is needed. Through measuring the surface EMG (sEMG) and analysis, the physical workload in overhead lifting posture is presented in quantitative manner. By normalizing sEMG activities with maximal voluntary contraction (MVC), the inter-subject variability is reduced. In all muscles, %MVC increased as the weight of lifting object increases. In anterior deltoid muscle, the %MVC was 3-4 times higher than the other muscles which imply that this muscle performs the major role in the overhead lifting posture. In fatigue analysis, %MVC and the mean frequency in muscle of anterior deltoid changed markedly when compared with other muscles. Through the suggested procedures and analysis, the physical workload for a specific posture can be represented in quantitative way but the clinical meaning for the value should be investigated further.

• 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.

• Proceedings of the KOSOMBE Conference
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• v.1990 no.05
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• pp.73-78
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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 which 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 shown that the proposed network is appropriate in recognizing the function of EMG signal.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.2
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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.

• Journal of the Korea Safety Management & Science
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• v.8 no.1
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• pp.43-52
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• 2006

The goal of this study is to investigate the ergonomic factors in designing or selecting the hand tool handle. Electromyogram (EMG) were measured for various wrist postures and handle sizes under two loading conditions. Anthropometric data were measured and the correlation with EMG measurement data were analyzed. Investigations of this study show that wrist posture should be neutral for minimum muscle tension and optimum handle size can be found by measuring the EMG measurement data. It show that hand width and EMG measurement data is greatly correlated also. This study can be a guide of designing or selecting a hand tool, but further study with large sample sizes and various groups is needed for making general conclusion.

• Journal of Korean Physical Therapy Science
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• v.18 no.3
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• pp.63-69
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• 2011

Purpose : The purpose of study is activation of lumbar multifidus muscle by needle EMG during shoulder flexion in chronic low back pain patients. The subject were consisted of 10 women patients with chronic low back pain and healthy asymtomatic subject 10 women. Methods : 10 women patients with chronic low back pain and healthy asymptomatic subject 10 women is voluntary participated for the research. Subjects were positioned in standing. The needle EMG were measured activation of multifidus. Needle electrode was used to 28 gauge. The shoulder flexion movement used to activate the multifidus was then measured. Results : Results of the analysis showed that asymptomatic subjects had significantly larger multifidus muscle activation compared with CLBP subjects during shoulder flexion. Conclusion : This study will be used as multifidus measurement method of patient with chronic LBP. The multifidus muscle in chronic LBP patient clinical significance. Most of chronic LBP patients have multifidus contraction pattern. Therefore chronic LBP patients necessary multifidus activation measurement with needle EMG.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.379-386
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• 1996

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) and hidden Markov models (HMM's) hybrid classifier. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or vari- ous time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrasts the multilayer feedforward networks are suitable for static patterns. Ank a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of MLP and HMM algorithms that might lead to further improved EMG recognition systems.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.378-381
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• 2002

In this paper, we proposed an electromyography(EMG) based control method of a virtual robot arm as an adaptive human supporting system or remote control system, which consists of an shoulder control part, elbow control part, and wrist control part. The system uses four surface electrodes to acquire the EMG signal from operator. It is shown from the experiments that the EMG patterns during arm motions can be classified sufficiently by using SOM and LVQ. The interface system based on PC environment is constructed to 3-D graphic user interface(GUI) program. Experimental results show that proposed method obtains approximately 94 percent of success in classification.