• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.06a
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• pp.227-228
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• 2008

• Journal of the Korean Society for Precision Engineering
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• v.17 no.11
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• pp.36-42
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• 2000

• Journal of Biomedical Engineering Research
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• v.25 no.6
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• pp.471-478
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• 2004

Electromyogram (EMC) signal generated by voluntary contraction of muscles is often used in a rehabilitation devices such as an upper limb prosthesis because of its distinct output characteristics compared to other bio-signals. This paper proposes an EMG-based human-computer interface (HCI) for the control of the above-elbow prosthesis or the wheelchair. To control such rehabilitation devices, user generates four commands by combining voluntary contraction of two different muscles such as levator scapulae muscles and flexor-extensor carpi ulnaris muscles. The muscle contraction is detected by comparing the mean absolute value of the EMG signal with a preset threshold value. However. since the time difference in muscle firing can occur when the patient tries simultaneous co-contraction of two muscles, it is difficult to determine whether the patient's intention is co-contraction. Hence, the use of the comparison method using a single threshold value is not feasible for recognizing such co-contraction motion. Here, we propose a novel method using double threshold values composed of a primary threshold and an auxiliary threshold. Using the double threshold method, the co-contraction state is easily detected, and diverse interface commands can be used for the EMG-based HCI. The experimental results with real-time EMG processing showed that the double threshold method is feasible for the EMG-based HCI to control the myoelectric prosthetic hand and the powered wheelchair.

• Journal of Biomedical Engineering Research
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• v.20 no.3
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• pp.299-305
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• 1999

In thls paper, the representative beal textraction algorIthms for the diagnostic parameter extraction in noisy signal were compared. We used the avernge, median, mode, and trmmed mean to calculale the central tendency. In our experimenl, we have restricted to four kinds of noises -EMG noise, 60Hz powerline inlerference, ahrupl baseline shift, and baselme drift due to respimtion-which were commonly occurred in ECG mgnal, then we have calculated signal-to-noise ratios(SNRs) for the ECG corrupted with each noise and all noises together. As the result of this paper, we have proved that the average method has super lor performance than the others in the ECG corrupted wilh EMG noise. When the signal mcludes extreme value such as abrupt baseline shIft, the median, mode, trimmed mean methods have supenor performance in the SNR ratios. Especially when the ECG corrupted with baseline drift due to respirallon, the trimmed mean method was most efficient because ST level change was 0 V.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.54-61
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• 2009

This paper presents a new electromyogram (EMG) pattern recognition method based on the Hierarchical Temporal Memory (HTM) algorithm which is originally devised for image pattern recognition. In the modified HTM algorithm, a simplified two-level structure with spatial pooler, temporal pooler, and supervised mapper is proposed for efficient learning and classification of the EMG signals. To enhance the recognition performance, the category information is utilized not only in the supervised mapper but also in the temporal pooler. The experimental results show that the ten kinds of hand motion are successfully recognized.

• Proceedings of the ESK Conference
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• 1997.04a
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• pp.190-195
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• 1997

Medilog SAC847 수면종합분석시스템을 이용하여 침대와 온돌에서 성인 남 자가 수면시 피험자의 뇌전도, 턱근전도, 다리근전도, 심전도, 안전도(눈) 등의 생리신호를 동시 측정하여수면단계기록국제기준에 근거하여 NREM 4단 계와 REM 단계의 수면시간을 산정 하였다. 수면감을 평가하기 위하여 침대 와 온돌에서 수면시 입면지연시간과 NREM 단계4의 수면시간 비중을 고찰한 결과 침대수면이 온돌수면보다 입면지연 시간이 더짧게 나타났으며 나머지 NREM 4단계와 REM 단계에 소요된 수면시간은 서로 비슷하게 나타났다. 본 연구를 통한 수면생리신호 분석연구는 쾌적침대개발에 활용하고자 한다.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.2
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• pp.126-133
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• 2023

In this study, electromyography was obtained in the six muscle areas that move the joints of the two legs, and by analyzing it, an exercise robot system capable of gait rehabilitation was proposed in consideration of the individual's muscle state. Through this, the system was constructed to prevent the effect of exercise from decreasing because the patient's will was not reflected when walking exercise was simply provided automatically. As a result of the evaluation of the developed system, it was confirmed that the pedestrian rehabilitation robot system manufactured through this study had performance suitable for the design requirements, and it was also confirmed that the usability evaluation was comprehensively satisfactory. The results of this study are thought to be of great help to patients who are having difficulty in gait rehabilitation, and are believed to be helpful in the development of electromyography signal-based gait robot systems.

• Journal of IKEEE
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• v.23 no.2
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• pp.727-734
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• 2019

This paper confirms whether the movement or specific operation of the muscles in the process of transferring a person from the brain can find a signal showing an essential feature of a certain part of the brain. As a rule, the occurrence of EEG(Electroencephalogram) changes when a signal is received from a specific action or from an induced action. These signals are very vague and difficult to distinguish from the naked eye. Therefore, it is necessary to define a signal for analysis before classification. The EEG form can be divided into the alpha, beta, delta, theta and gamma regions in the frequency ranges. The specific size of these signals does not reflect the exact behavior or intention, since the band or energy difference of the activated frequencies varies depending on the EEG measurement domain. However, if different actions are performed in a specific method, it is possible to classify the movement based on EEG activity and to determine the EEG tendency affecting the movement. Therefore, in this article, we first study the EEG expression pattern based on the activation of the left and right biceps EMG, and then we determine whether there is a significant difference between the EEG due to the activation of the left and right muscles through EEG. If we can find the EEG classification criteria in accordance with the EMG activation, it can help to understand the form of the transmitted signal in the process of transmitting signals from the brain to each muscle. In addition, we can use a lot of unknown EEG information through more complex types of brain signal generation in the future.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.630-632
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• 2023

어텐션 메커니즘은 딥 뉴럴네트워크에 결합하여 언어 생성 모델에서 성능을 개선하였고, 이러한 성공은 다양한 신호처리 분야에 응용 및 확장되고 있다. 특정 입력 신호 부분에 선택적으로 집중함으로써, 어텐션 모델은 음성 인식, 이미지와 비디오 처리, 그리고 생체인식 등의 분야에서 더 높은 성능을 보여주고 있다. 어텐션 기반 모델은 심전도 신호를 이용한 개인식별 및 부정맥검출, 뇌파도 신호를 이용한 발작유형분류 및 수면 단계 분류, 근전도 신호를 이용한 제스처 인식 등에 사용되고 있다. 어텐션 메커니즘은 딥 뉴럴네트워크의 해석 가능성과 설명 가능성을 향상시키기 위해 사용되기도 한다. 신호 처리 분야에서의 어텐션 모델 연구는 지속적으로 진행 중이며, 다른 분야에서의 잠재력 탐구에 대한 관심이 높아지고 있다. 따라서 본 논문은 어텐션 메카니즘을 이용한 생체신호처리 연구 동향 분석을 수행한다.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.10 no.2
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• pp.155-162
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• 2016

The purpose of this study was to determine the walking imbalance using the EMG(electromyogram). To confirm the effectiveness of the proposed encoder and acceleration, EMG sensor based gait imbalance determination system. This experiment was carried out to evaluation with a healthy adult male to 10 people. The Encoder device is attached to the hip and knee joint in order to measure the gait signal. The Accelerometer sensors are attached on the ankle. The EMG sensors are attached on the vastus lateralis and anterior tibialis. SI(Symmetry Index) was used as an index for determining the gait imbalance. To confirm if the judgment has been made correctly, the heel, regarded as the cause of unbalanced ambulation, was adjusted from 0 cm to 6 cm with intervals of 1.5 cm. In the cases of the encoder and the EMG, the difference of 0 cm and 1.5 cm is determined into normal walk but the other difference is distinguished into gait imbalance. In the case of the accelerometer, the difference of 0 cm, 1.5 cm and 3 cm is determined into normal walk but the other difference is distinguished into gait imbalance.