• 대한의용생체공학회:의공학회지
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• 제29권3호
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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.

• 한국정밀공학회지
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• 제31권11호
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• pp.1051-1058
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• 2014

In this study, we investigated the improvement effect of obesity by treatment with a developed low frequency electrical stimulation system. Thirty female in their 20's as an experiment subjects divided 3 groups(control, commercialized device, developed device) were treated with electrical stimulation on abdomen for 4 weeks. The body weight, body mass index(BMI), waist-hip ratio(WHR), muscle strength, muscle(transverse abdominis(TrA), internal obliquus abdominis(IO), external obliquus abdominis(EO)), fat thickness and abdominal surface temperature were measured by electromyogram(EMG), ultrasound and digital infrared thermal image(DITI). In the result, the body weight and BMI were decreased. Subcutaneous abdominal fat were significantly reduced after 4 weeks. The muscle strength and TrA muscle thickness was increased 13.2%(p<0.05), and 35.5%, respectively. The fat thickness showed decrease in abdomen (p<0.05). Infrared measurement on abdominal surface temperature as a parameter of improvement in blood circulation was significantly increased(p<0.05). Therefore, the low frequency stimulation showed positive effects on parameters of the obesity improvement.

• 전자공학회논문지SC
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• 제47권3호
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• pp.28-39
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• 2010

본 논문에서는 표면 근전도(surface electromyogram, SEMG)를 이용하여 운동단위(motor unit, MU)의 위치(location)를 추정하는 새로운 방법을 제안하였다. 운동단위의 위치에 따라 운동단위 활동전위(motor unit action potential, MUAP), 나아가서는 표면 근전도의 크기(amplitude)가 변화하므로 운동단위의 위치 추정은 근력 추정에 있어서 중요하다. 제안된 방법은 표면근전도 시뮬레이션을 통해 취득한 기준 신호와 3 채널 표면전극을 이용하여 검출한 표면 근전도 신호를 비교하여 운동단위의 위치를 추정하는 방법이다. 운동단위 위치 추정의 정확도를 파악하기 위하여 컴퓨터 시뮬레이션을 통하여 취득한 MUAP를 본 연구에서 제안한 방법 및 기존 방법들을 적용하여 확인하였다. 시뮬레이션 결과 8[mm] 위치에 운동단위가 위치할 경우 본 논문에서 제안한 운동단위 위치 추정 방법은 0.01[mm]의 평균 추정 오차를 보였다. 반면에 Roeleveld가 제안한 추정 방법은 2.33[mm]의 평균 추정 오차를 보였으며 Akazawa가 제안한 추정 방법은 1.70[mm]의 평균 추정 오차를 보여 본 연구에서 제안한 운동단위 위치 추정 방법이 기존의 방법들에 비하여 더 정확한 위치 추정이 가능하였다.

• 한국소음진동공학회논문집
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• 제21권10호
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• pp.934-939
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• 2011

This paper experimentally deals with the relationship between the ankle electromyogram(EMG) and walking motion in order to activate the ankle joint of a walking-assistance robot for rehabilitation. Based on the anatomical structure and motion pattern of an ankle joint, major muscles were selected for EMG measurements. Surface EMG signals were monitored for several human bodies at various stride distances and stride frequencies. Root-mean-squared magnitude of EMG signals were related with the walking conditions. It appeared that the magnitude of the ankle EMG signal was linearly proportional to the stride distance and stride frequency, and thus to the walking speed.

• PNF and Movement
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• 제10권4호
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• pp.25-31
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• 2012

Purpose : The purpose of present study was to investigate the effects of proprioceptive neuromuscular facilitation (PNF) lifting on contralateral leg muscle activities in a seated position. Methods : Twenty healthy subjects were recruited for this study. Lifting was performed from each of the three position. An surface electromyogram (EMG) was used to record the EMG activities from vastus medialis (VM), biceps femoris (BF), tibialis anterior (TA), and gastrocnemius medialis (GM) in contralateral leg muscle. The data were analyzed using a repeated measures of one-way analysis of variance (ANOVA) with post-hoc Bonferroni's correction to determine the statistical significance. Results : The results of this study were summarized as follows: In comparison to the start position, percentage maximal voluntary isometric contraction (%MVIC) values of the VM, TA and GM demonstrated a significantly higher activities in the end position(p<.05). Conclusion : The result shows that contralateral leg muscles activities significantly more increase in the end position when PNF lifting was applied. Therefore, this study will be used to prove effect of indirect approach for the stability and strengthening in patients with leg impairments.

• 재활복지공학회논문지
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• 제6권1호
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• pp.67-73
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• 2012

본 논문에서는 표면 근전도 신호를 사용하여 손목 움직임의 동작을 분류하기 위해 인공 신경 회로망(ANN : Artificial Neural Network)기반의 동작 분류 알고리즘을 제안한다. 손목 움직임에 무리가 없는 20~30대 성인 26명을 대상으로 척측 수근 굴근과 척측 수근 신근에 부착한 2채널의 전극으로부터 표면 근전도 신호를 취득하고, 취득한 근전도로부터 손목의 굴곡, 신전, 내전, 외전, 휴식 다섯 동작을 인식한다. 빠른 처리 속도를 위해 획득한 신호로부터 시간 영역에서의 특징점을 추출하고 ANN을 이용한 동작 분류에 사용된다. 특징점으로 DAMV, DASDV, MAV, RMS를 사용하였으며, ANN 기반의 동작 분류의 인식율은 DAMV는 98.03%, DASDV는 97.97%, MAV는 96.95%, 그리고 RMS는 96.82%의 정확도를 나타낸다.

• 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.

• 음성과학
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• 제11권1호
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• pp.55-64
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• 2004

This study was to determine the effects of a massage and oro-facial exercise program on spastic dysarthric patients' lip muscle function using an electromyogram (EMG). Three subjects with Spastic Dysarthria participated in the study. The surface electrodes were positioned on the Levator Labii Superior Muscle (LLSM), Depressor Labii Inferior Muscle (DLIM), and Orbicularis Oris Muscle (OOM). To examine lip muscle function improvement, the EMG signals were analyzed in terms of RMS (Root Mean Square) values and Median Frequency. In addition, the diadochokinetic movements and the rate of sentence reading were measured. The results revealed that the RMS values were decreased and the Median Frequency moved to a high frequency area. Diadochokinesis and sentence reading rates were improved.

• 센서학회지
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• 제25권5호
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• pp.365-370
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• 2016

The study about VR (Virtual Reality) has been done from the 1960s, but technical limits and high cost made VR hard to commercialize. However, in recent, high resolution display, computing power and 3D sensing have developed and hardware has become affordable. Therefore, normal users can get high quality of immersion and interaction. However, HMD devices which offer VR environment have high latency, so it disrupts the VR environment. People are usually sensitive to relative latency over 20ms. In this paper, as adding the Electromyogram (EMG) sensors to typical IMU sensor only system, the latency reduction method is proposed. By changing software and hardware components, some cases the latency was reduced significantly. Hence, this study covers the possibility and the experimental verification about EMG sensors for reducing the latency.

• 로봇학회논문지
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• 제15권1호
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• pp.62-69
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• 2020

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.