• Journal of the Ergonomics Society of Korea
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• v.27 no.2
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• pp.9-14
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• 2008

Coherence can be used to evaluate the functional cortical connections between the motor cortex and muscle. This study is to find coherence between EEG (electroencephalogram) and EMG (electromyogram) evoked by movement of a hand. Seven healthy participants were asked to perform thirty repetitive movement of right hand for ten seconds with rest for ten seconds. Specific feature of EEG components has been extracted by ICA (independent component analysis) and coherence between EEG and EMG was analyzed from data measured EEG in five local areas around central part of head and EMG in flexer carpri radialis muscle during grabbing movement. Coherence between EEG and EMG was successfully obtained at 0.025 confidence limit during hand movement and showed significant difference between rest and movement at 13-18Hz.

• The Journal of Korea Robotics Society
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• v.19 no.3
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• pp.254-265
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• 2024

Pattern recognition for surface electromyogram (sEMG) suffers from its nonstationary and stochastic property. Although it can be relieved by acquiring new training data, it is not only time-consuming and burdensome process but also hard to set the standard when the data acquisition should be held. Therefore, we propose an adaptive sEMG pattern recognition algorithm using principal component analysis. The proposed algorithm finds the relationship between sEMG channels and extracts the optimal principal component. Based on the relative distance, the proposed algorithm determines whether to update the existing patterns or to register the new pattern. From the experimental result, it is shown that multiple patterns are generated from the sEMG data stream and they are highly related to the motion. Furthermore, the proposed algorithm has shown higher classification accuracy than k-nearest neighbor (k-NN) and support vector machine (SVM). We expect that the proposed algorithm is utilized for adaptive and long-lasting pattern recognition.

• ETRI Journal
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• v.36 no.1
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• pp.99-105
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• 2014

This paper presents a new locomotion mode recognition method based on a transformed correlation feature analysis using an electromyography (EMG) pattern. Each movement is recognized using six weighted subcorrelation filters, which are applied to the correlation feature analysis through the use of six time-domain features. The proposed method has a high recognition rate because it reflects the importance of the different features according to the movements and thereby enables one to recognize real-time EMG patterns, owing to the rapid execution of the correlation feature analysis. The experiment results show that the discriminating power of the proposed method is 85.89% (${\pm}2.5$) when walking on a level surface, 96.47% (${\pm}0.9$) when going up stairs, and 96.37% (${\pm}1.3$) when going down stairs for given normal movement data. This makes its accuracy and stability better than that found for the principal component analysis and linear discriminant analysis methods.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.105-114
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• 2008

The purpose of this study was to compare of muscles in clean and jerk techniques between 69kg class(n=3), 58kg class(n=3) in high school female weight lifters using EMG(electromyographic) system. EMG analysis were executed on 6 major muscles and dividing clean and jerk techniques into 6 phases. In that result, in the difference by weight, it was shown that EMG value increased gradually as the weight is raised of all muscles group & phases in 58kg class. In EMG signal scale by classes, it was shown that EMG signal scale didn't increase according to class & weight. In the result of this study, that EMG value was inconsistent in 69kg class is showing that the consideration of the technical factor together with muscle power has positive affect more on the performance improvement in the heavy class.

• Journal of Korean Institute of Industrial Engineers
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• v.9 no.2
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• pp.9-25
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• 1983

The purpose of this study was to film up computerized analyses for both kinematic posture(film analysis) and muscle dynamics (EMG) during a weight-lifting motion. (Snatch, Clean and Jerk) Using a motor drive camera (3.5 frames/sec) and a Location Analyzer, motion tracks of 13 landmarks, which were attached to the major joints, during the motion were converted into digital values. At the same time, EMG amplitudes from 11 major muscle groups were recorded. Recorded data were processed via analog/hybrid computer (ADAC 480) and digital computer (PDP 11/44). Landmark locations and EMG amplitude were integrated by a computerized routine. Computer output included graphic reproductions on sepuential dislocations of body segments, center of gravity of body segments and the associated changes on EMG amplitude such as % EMG's of major muscle group during a weight lifting motion. The results strongly suggest that the computerized motion-EMG integration can provide a further working knowledge in selection and in training of workers and athletes. Suggestions for a further study include additional device for velocity measurement, expansion of the link model for biomechanical analysis and other implementations necessary for athletic application.

• Journal of Biomedical Engineering Research
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• v.32 no.4
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• pp.319-327
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• 2011

Skeletal muscle fatigue is defined as a 'any reduction in the maximal capacity to generate force or power output', and is the reduction of oxygen consumption and by-product of metabolism. For the muscle fatigue therapy, low level laser has been introduced that leads the mitochondrial respiratory and attributes the muscle fatigue recovery. This study analyzed the muscle fatigue signals from electromyography(EMG) during low-level laser therapy (LLLT). Healthy subjects performed voluntary elbow flexion-extension excercise and received placebo LLLT and active LLLT using a 830 nm laser diode. Then, EMG were measured for the evaluation of muscle fatigue. The acquired EMG data were analyzed with median frequency and short time fourier transform methods. The results showed that the LLLT had a significant symptomatic relief of muscle fatigue based on the EMG frequency analysis. Therefore, the muscle fatigue analysis with EMG signals can be applied to quantitative evaluation for the monitoring of LLLT effects.

• Korean Journal of Occupational Health Nursing
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• v.8 no.2
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• pp.156-161
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• 1999

The EMG(Electromyography) analysis was used to identify the fact the degree of inclined step was selected as dependent variable and feet muscle fatigue was selected as a independent variable. In a final result from EMG test. the shift in median frequency (MF) with 20, 25, 30, 35 degree of inclined steps indicated that 30 degree step was identified as most effective for a decrease in feet muscle fatigue. In a department store, 80% of the workers are female standing sales workers. They work at standing on average 10 hours per day. They performed heavy duty jobs such as lifting, lowering. packing and carrying heavy materials. Furthermore, even though they have work shoes, they usually use various kind of high heels. Eventually, this situation develops low-back-pain (LBP) problems for female workers. In conclusion, it is recommended that a particular branch in a department store claimed this step can effectively to circulate blood and significantly decrease feet muscle fatigue in lower extremity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.757-762
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• 2012

In this paper, the multi feature extraction algorithm for estimation of wrist movements based on Electromyogram(EMG) is proposed. For the extraction of precise features from the EMG signals, the difference absolute mean value(DAMV), the mean absolute value(MAV), the root mean square(RMS) and the difference absolute standard deviation value(DASDV) to consider amplitude characteristic of EMG signals are used. We figure out a more accurate feature-set by combination of two features out of these, because of multi feature extraction algorithm is more precise than single feature method. Also, for the motion classification based on EMG, the linear discriminant analysis(LDA), the quadratic discriminant analysis(QDA) and k-nearest neighbor(k-NN) are used. We implemented a test targeting twenty adult male to identify the accuracy of EMG pattern classification of wrist movements such as up, down, right, left and rest. As a result of our study, the LDA, QDA and k-NN classification method using feature-set with MAV and DASDV showed respectively 87.59%, 89.06%, 91.75% accuracy.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.1
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• pp.63-73
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• 2015

The purpose of this study was to examine EMG activities and VMO/VL ratio of the vastus medialis oblique, and vastus lateralis during step up exercise according to ankle and knee positions in soccer players with patellofemoral pain syndrome. Methods: Subject(patellofemoral pain syndrome, PFPS: n=8 and without PFPS, non PFPS; NPFPS: n=8) perfomed step up exercise at each knee and ankle position(knee flexion $30^{\circ}$, $60^{\circ}$, and $90^{\circ}$, ankle internal rotation $30^{\circ}$, neutral, and external rotation $30^{\circ}$) while EMG activity was collected. The EMG signals were expressed by the % maximal voluntary isometric Contraction(%MVIC) values. Statistical analysis consisted of two way repeated measures analysis of variance with post hoc analysis. Results: Main results were as follows: 1) EMG of VMO, and VL was tend to be lower in PFPS compared to NPFPS. 2) EMG of VMO and VL with knee flexrion $60^{\circ}$ was significantly higher the results with knee flexion $30^{\circ}$, and $90^{\circ}$. VMO and VL with ankle external rotation $30^{\circ}$ was significantly higher the results with internal rotation $30^{\circ}$ and neutral position. Conclusion: Considering the EMG activity was reduced due to the to the PFPS and that performing step up with knee flexion $60^{\circ}$ with ankle external rotation $30^{\circ}$ position may provide the most effective condition for patients with patellofemoral pain syndrome.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.743-746
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• 2011

An implementation for surface EMG network analysis and vertical control system of robotic arm is presented in this paper. The transmembranes are simulated by equivalent circuit and cable equation for propagation to be converted to circuit networks. The implementation is realized to be derived from the detecting EMG signal from 3 electrodes, and EMG transmembrane signals of human arm muscles are detected by several surface electrodes, high performance amplifier and filtering, converting analog to digital data and driving a servomotor for spontaneous robotic arm. The system is experimented by monitoring multiple steps vertical control angles corresponding to biceps muscle movement. The experimental results are that the vertical moving control level is measured to around 2 degrees and mean error ranges are lower 5%.