• 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 Biomedical Engineering Research
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• v.29 no.3
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• pp.205-211
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• 2008

A method of detecting the specific patterns related to hand motions using the surface EMG(electromyogram) on an arm is proposed and tested. To do this, we obtain separately modeling parameters based on the LP, Prony estimator, and calculate the latency shift value between channels by cross-correlation function. Then, the coefficients and latency shift value are applied to the detection method to classify the EMG signals related to hand motions. Compared with the conventional methods, the present method are more useful to detect the motion intention of the user as an input device in the mobile and wearable computing environments. And, We expect that the results of this study are helpful in the development of rehabilitation devices for the handicapped.

• 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 Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.17-22
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• 2009

In this paper, we extract well-known parameters such as zero crossing rate(ZCR), low band energy(Band) and median frequency(MDF) from surface electromyogram (EMG), and compare to evaluate themselves as measures for fatigue. In experiments, 3 males and 3 females volunteered to participate in surface EMG recordings placed on the biceps brachii and each recording experiment continued until exhaustion.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.1
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• pp.8-12
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• 2010

In this paper, we studied on AR model order decision for extraction of EMG median frequency by t-test and ANOVA and comparison of median frequency. And we extracted well-known parameters such as zero crossing rate(ZCR), low band energy(Band) and median frequency(MDF) from surface electromyogram (EMG). And we compared to evaluate themselves as measures for fatigue.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.395-401
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• 1996

In this paper a new neural network model, time delayed counterpropagation neural networks (TDCPN) which have high recognition rate and short total learning time, is proposed for electromyogram(EMG) recognition. Signals the proposed model increases the recognition rates after learned the regional temporal correlation of patterns using time delay properties in input layer, and decreases the learning time by using winner-takes-all learning rule. The ouotar learning rule is put at the output layer so that the input pattern is able to map a desired output. We test the performance of this model with EMG signals collected from a normal subject. Experimental results show that the recognition rates of the suggested model is better and the learning time is shorter than those of TDNN and CPN.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.1031-1033
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• 2017

본 논문에서는 EMG(Electromyogram) 신호 기반의 재활 치료용 VR(Virtual Reality) 플랫폼을 제안한다. EMG 신호는 근육의 움직임을 확인할 수 있는 생체 신호로, EMG 신호를 활용하면 근육에 직접적인 움직임이 없어도 환자의 행동 의도를 확인할 수 있다. 본 논문에서는 EMG 신호를 이용하여 환자의 근육 움직임을 확인하며, 해당 움직임을 나타내는 VR 콘텐츠에 대한 제안과 실제 제작 콘텐츠를 소개한다. 실험 결과는 실제 근육 움직임에 대한 인식률을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.493-494
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• 2013

본 논문에서는 컬-덤벨 운동의 연속, 반복적인 근 수축/이완 동작에 대한 전환 시점을 검출하기 위한 EMG(Electromyogram) 신호 분석 모듈을 설계하였으며 이에 대한 안드로이드 플랫폼 기반의 테스트 프로그램을 구현하였다. 제안하는 모듈로는 무산소 운동 분석에의 활용도가 높은 EMG 신호를 기반으로 근 수축/이완의 전환 시점을 95% 검출하며, 이를 개선 및 확장함으로써 현재 상용화 및 보급화된 운동 가이드 시스템에서 제공할 수 없었던 무산소 운동의 체계적 가이드를 제공할 수 있을 것으로 기대된다.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.213-220
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• 2024

Fall detection and prevention technologies play a pivotal role in ensuring the well-being of individuals, particularly those living independently, where falls can result in severe consequences. This paper addresses the challenge of accurate and quick fall detection by proposing a Bayesian probability-based measure applied to surface electromyography (sEMG) signals. The proposed algorithm based on a Bayesian filter that divides the sEMG signal into transient and steady states. The ratio of posterior probabilities, considering the inclusion or exclusion of the transient state, serves as a scale to gauge the dominance of the transient state in the current signal. Experimental results demonstrate that this approach enhances the accuracy and expedites the detection time compared to existing methods. The study suggests broader applications beyond fall detection, anticipating future research in diverse human-robot interface benefiting from the proposed methodology.

• Journal of Acupuncture Research
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• v.15 no.2
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• pp.319-329
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• 1998

The purpose of this study was finding the pain inhibitory effect of acupuncture based on rataining time at LI -4. The pain at dentes incisor was evoked by noxious electric stimulation and digastric electromyogram(dEMG) changes based on time interval were measured. To do this, the opioid antagonist was administered intraperitoneally and four groups were made for convenience. Without naloxone, dEMG was changed by either retaining the needle for 40 minutes (Group I) or by lifting and thrusting the needle (Group II). With naloxone administration, dEMG was changed by either retaining the needle for 40 minutes (Group III) or by lifting and thrusting the needle (Group IV). The results are as following 1. The pain inhibitory effect of acupuncture at LI -4 was expressed best in Group I. 2. The pain inhibitory effect was somewhat expressed in Group II but the effect was smaller than Group I. 3 .In Groups III and IV, the pain inhibitory effect was not expressed. The overall result should be the foundation for the further studies to figure out the underlying mechanism of acupuncture. In addition, it is assumed that the results will be useful for optimal retaining time of acupucture for its maximal effect.