• 로봇학회논문지
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• 제14권3호
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1944-1947
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• 2005

In this paper, the control of a prosthetic arm using the flex sensor signal is described. The flex sensors are attached to the biceps and triceps brchii muscle. The signals are passed a differential amplifier and noise filter. And then the signals are converted to digital data by PCI 6036E ADC. From the data, position and velocity of arm joint are obtained. Also motion of the forearm - flexion and extension, the pronation and supination are abstracted from the data by proposed algorithm. A two D.O.F arm with RC servo-motor is designed for experiment. The arm length is 200 mm, weight is 4.5 N. The rotation angle of elbow joint is $120^{\circ}$. Also the rotation angle of the wrist is $180^{\circ}$. Through the experiment, we verified the possibility of the prosthetic arm control using the flex sensor signal. We will try to improve the control accuracy of the prosthetic arm continuously.

• 대한기계학회논문집A
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• 제41권5호
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• pp.415-420
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• 2017

과소 구동 전동의수는 가벼우며 비교적 경제적이라는 장점이 있다. 본 연구에서는 적응파지가 가능한 과소 구동 전동의수를 대상으로 경제적인 파지력 제어 시스템을 제안하였다. 근전도 신호로 구동되는 메인 케이블의 장력으로 파지력이 결정되므로 장력에 따라 사용자의 피부에 부착된 진동모터를 구동하는 촉감기반 피드백 시스템을 구성하였다. 진동 신호에 대한 사용자의 감각적 판단을 기반으로 파지력을 추정하고 제어하기 위하여 파지력과 진동 신호 간의 적절한 변환 관계를 수립하고 시제품 성능시험을 하였다. 최소한의 훈련으로 사용자들은 비교적 정확하게 파지력을 제어할 수 있었다.

• 로봇학회논문지
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• 제13권4호
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• pp.265-271
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• 2018

The recent prosthetic technologies pursue to control multi-DOFs (degrees-of-freedom) hand and wrist. However, challenges such as high cost, wear-ability, and motion intent recognition for feedback control still remain for the use in daily living activities. The paper proposes a multi-channel knit band sensor to worn easily for surface EMG-based prosthetic control. The knitted electrodes were fabricated with conductive yarn, and the band except the electrodes are knitted using non-conductive yarn which has moisture wicking property. Two types of the knit bands are fabricated such as sixteen-electrodes for eight-channels and thirty-two electrodes for sixteen-channels. In order to substantiate the performance of the biopotential signal acquisition, several experiments are conducted. Signal to noise ratio (SNR) value of the knit band sensor was 18.48 dB. According to various forearm motions including hand and wrist, sixteen-channels EMG signals could be clearly distinguishable. In addition, the pattern recognition performance to control myoelectric prosthesis was verified in that overall classification accuracy of the RMS (root mean squares) filtered EMG signals (97.84%) was higher than that of the raw EMG signals (87.06%).

• 대한두개안면성형외과학회지
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• 제11권1호
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• pp.53-57
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• 2010

연부조직만으로 경구개를 재건하는데 있어서는 환자군을 적절히 선택하는 것이 중요하며 골재건이 필요하지 않은 Okay 분류 Ia와 Ib가 주요한 적응증이 된다. 하악이나 구강저부 결손을 재건하는 것과는 다르게 경구개 결손은 구강과 비강 점막층을 동시에 수복할 수 있는 피판이 이상적이다. 이중 저자들은 전완유리피판에 전상판화 방법을 좀 더 안정적으로 시행, 경구개 전층을 성공적으로 재건하였으며, 특히 저작과 연하 등 기능적 측면뿐 아니라 경구개 및 비강의 점막을 함께 복원할 수 있는 해부학적인 장점이 있는 피판임을 확인하여 문헌고찰과 함께 보고하는 바이다.

• 재활복지공학회논문지
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• 제5권1호
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• pp.95-101
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• 2011

본 논문에서는 의수환자의 일상생활을 고려한 1-자유도 동작을 손을 쥐고 폄으로 정의하고, 두 동작에 대한 근전도 패턴 분류를 위한 가우시안 혼합 모델 기반의 근전도 패턴 분류 알고리즘을 제안한다. 근전도 패턴 분류 알고리즘의 핵심이 되는 근전도 신호의 특징점 추출을 위하여 근전 신호의 진폭 특성을 고려하는 절대차분평균치(DAMV)와 평균절대값(MAV)을 사용한다. 또한 동작에 대한 근전 신호의 진폭 특성을 보다 명확히 구분하기 위하여 D_DAMV와 D_MAV를 제안한다. 본 논문에서는 4명의 성인남성을 대상으로 실험을 실시하였고, 두 동작에 대한 근전도 패턴의 정확한 분류 여부를 확인하였다.

• Archives of Reconstructive Microsurgery
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• 제20권1호
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• pp.8-13
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• 2011

Purpose: A palatal defect following maxillectomy can cause multiple problems like the rhinolalia, leakage of foods into the nasal cavity, and hypernasality. Use of a prosthetic is the preferred method for obturating a palate defect, but for rehabilitating palatal function, prosthetics have many shortcomings. In a small defect, local flap is a useful method, however, the size of flap which can be elevated is limited. In 12 cases of palatomaxillary defect, we used various microvascular free flaps in reconstructing the palate and obtained good functional results. Method: Between 1990 and 2004, 12 patients underwent free flap operation after head and neck cancer ablation, and were reviewed retrospectively. Among the 12 free flaps, 6 were latissimus dorsi myocutaneous flaps, 3 rectus abdominis myocutaneous flaps, and 3 radial forearm flaps. Result: All microvascular flap surgery was successful. Mean follow up time was 8 months and after the follow up time all patients reported satisfactory speech and swallowing. Wound dehiscence was observed in 4 cases, ptosis was in 1 case and fistula was in 1 case, however, rhinolalia, leakage of food, or swallowing difficultly was not reported in the 12 cases. Conclusion: We used various microvascular flaps for palatomaxillary reconstruction. For 3-dimensional flap needs, we used the latissimus dorsi myocutaneous flap to obtain enough volume for filling the defect. Two-dimensional flaps were designed with latissimus dorsi myocutaneous flap, rectus abdominis flap and radial forearm flap. For cases with palatal defect only, we used the radial forearm flap. In palatomaxillary reconstruction, we can choose various free flap techniques according to the number of skin paddles and flap volume needed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.468-472
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• 1992

In this paper, we classified electromyographic(EMG) signal for prothesis control using neural network. For this study fast Fourier transform(FFT) with ensemble averaged spectrum is applied to two-channeI EMG signal for biceps and triceps. We used the three layer network. And a cumulative back-propagation algorithm is used for classification of six arm functions, flexion and extension of elbow and pronation and supination of the forearm and abduction and adduction of wrist.

• 대한의용생체공학회:의공학회지
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• 제40권6호
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• pp.242-249
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• 2019

Forearm electromyography (EMG) generated by wrist movements has been widely used to develop an electrical prosthetic hand, but EMG generated by finger movements has been rarely used even though 20% of amputees lose fingers. The goal of this study is to improve the classification performance of different finger movements using a deep learning algorithm, and thereby contributing to the development of a high-performance finger-based prosthetic hand. Ten participants took part in this study, and they performed seven different finger movements forty times each (thumb, index, middle, ring, little, fist and rest) during which EMG was measured from the back of the right hand using four bipolar electrodes. We extracted mean absolute value (MAV), root mean square (RMS), and mean (MEAN) from the measured EMGs for each trial as features, and a 5x5-fold cross-validation was performed to estimate the classification performance of seven different finger movements. A long short-term memory (LSTM) model was used as a classifier, and linear discriminant analysis (LDA) that is a widely used classifier in previous studies was also used for comparison. The best performance of the LSTM model (sensitivity: 91.46 ± 6.72%; specificity: 91.27 ± 4.18%; accuracy: 91.26 ± 4.09%) significantly outperformed that of LDA (sensitivity: 84.55 ± 9.61%; specificity: 84.02 ± 6.00%; accuracy: 84.00 ± 5.87%). Our result demonstrates the feasibility of a deep learning algorithm (LSTM) to improve the performance of classifying different finger movements using EMG.