• 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.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.527-535
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• 2008

Many signal processing techniques have been described in the literature for estimating amplitude, frequency and duration variables of the surface EMG signal detected during constant voluntary contractions. They have been used in different application areas for the non-invasive assessment of muscle function. The main purpose of our research is to compare the most frequently used algorithms for information extraction from surface EMG signals under varying conditions in terms of the different window lengths, muscle contraction levels, muscles and subjects. In particular we focus on the issue of estimating the slope and intercept to resolve an linear regression line with utilizing real SEMG signals which represents voluntary contractions during thirty seconds.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.769-776
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• 2007

This paper proposes the human arm motion tracking algorithm based on the signal processing for surface EMG (electromyogram) sensors attached on both upper arm and shoulder. The signals acquired by using surface EMG sensors are processed with choosing the maximum in a short period, taking the absolute value, and filtering noises out with a low-pass filter. The processed signals are directly used for the motion generation of virtual arm in real time simulator. The virtual arm of simulator has two degrees of freedom and complies with the flexion and extension motions of elbow and shoulder. Also, we show the validity of the suggested algorithms through the experiments.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2687-2689
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• 2002

본 연구는 환자의 근육 상태를 표면 근전도(EMG, Electrocardiogram)를 통해 정량적으로 평가한 결과를 기반으로 적응 전기치료를 수행 시, 근전도 정량평가에 영향을 주는 심전도 신호를 독립요소 해석(ICA, Independent Component Analysis)을 이용하여 획득된 신호로부터 분리함으로써, 정확한 근전도 정량평가를 할 수 있도록 하는 것을 목적으로 한다. 실험 방법은 소스(source)를 근전도와 심전도 2개로 가정하고, 4 채널을 통하여 획득된 신호를 10 Hz-500 Hz의 대역통과 필터를 이용하여 필터링한 후, 1000 sample/sec로 샘플링하여 센서로 사용하였으며, JADE(Joint Approximate Diagonalization of Eigen-matrices) 알고리즘을 통하여 근전도 신호와 심전도 신호를 분리하였다. 알고리즘의 permutation ambiguity와 scaling ambiguity 특성 문제를 해결하기 위하여, 분리된 신호의 주파수 분석을 통하여 심전도와 근전도 신호로 구분하였으며, 인식된 근전도 신호의 크기를 센서 신호를 기준으로 복원하였다. 결론적으로 아날로그 및 디지털 필터와 달리 근전도의 신호의 왜곡을 극소화하면서도 심전도 신호를 분리해 냄으로써, 근전도를 통한 근육상태의 효과적인 평가가 가능하게 되었다.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.19-26
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• 2013

This paper propose a surface EMG signal based gait phase recognition method that selects features and channels adaptively. The proposed method can be used to control powered artificial prosthetic for lower limb amputees and can reduce overhead in real-time pattern recognition by selecting adaptive channels and features in an embedded device. The method can enhance the classification accuracy by adaptively selecting channels and features based on sensitivity and specificity of each subject because EMG signal patterns may vary according to subject's locomotion convention. In the experiments, we found that the muscles with highest recognition rate are different between human subjects. The results also show that the average accuracy of the proposed method is about 91% whereas those of existing methods using all channels and/or features is about 50%. Therefore we assure that sEMG signal based gait phase recognition using small number of adaptive muscles and corresponding features can be applied to control powered artificial prosthetic for lower limb amputees.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.48-57
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• 2003

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) with genetic algorithm and hidden Markov models (HMM's) hybrid classifier. Genetic Algorithms play a role of selecting Multilayer Perceptron's optimized initial connection weights by its typical global search. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or various 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 contrast, the multilayer feedforward networks are suitable for static patterns. And, 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 ANN and HMM algorithms that might lead to further improved EMG recognition systems.

• Journal of Biomedical Engineering Research
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• v.33 no.2
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• pp.65-71
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• 2012

In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).

• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.67-73
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• 2012

In this paper, Artificial Neural Network(ANN) based motion classification algorithm is proposed to classify wrist motions using surface electromyograms(sEMG). surface EMGs are obtained from two electrodes placed on the flexor carpi ulnaris muscle and extensor carpi ulnaris muscle of 26 subjects under no strain condition during wrist motions and used to recognize wrist motions such as up, down, left, right, and rest. Feature is extracted from obtained EMG signals in time domain for fast processing and used to classify wrist motions using ANN. DAMV, DASDV, MAV, and RMS were used as features and accuracies of motion classification based on ANN were 98.03% for DAMV, 97.97% for DASDV, 96.95% for MAV, 96.82% for RMS.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.1 no.1
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• pp.45-52
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• 2007

Recently, overweight that is the major cause of diseases is troubled about the serious social problems. To solve this problem, the need of measurement system for the overweight management has been increased. This paper is the study on measurement system. This system analyzes EMG (electromyograph) of the abdomen. Then, checks one's state of health and offers the right medical services wherever and whenever. To analyze EMG of the abdomen, this paper proposes the algorithms that are energy detection, feature detection, classification and recognition, etc. Measurement system based on the analysis algorithms of EMG appraises overweight and physical strength of the abdomen. It also provides the right estimation for one's state of health.