• Journal of Biomedical Engineering Research
• /
• v.20 no.3
• /
• pp.315-321
• /
• 1999

The purpose of this study is to develop a mathematical model for system identification in order to predIct muscle force based on eledromyographic signal. Therefore, a finding of the relalionship between characteristics of electromyographic signal and the corre spondng muscle force should be necessiiry through dynamic, joint model. To develop the dynamic joint model, the upper limb mcludmg the wrist and elbow joint has been considered. The kinematic and dynamic data, such as joint angular displacement, velocity, deceleration along with the moment of inertla, required to establish the dynamic model has been obtained by electrical flexible goniometer which has two degree-of-frcedoms. ln this model, muscle force can be predicted only electromyographs through the relationship between the integrated lorce and the mtegrated electromyographic signal over the duration of muscle contraclion in this study.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.7 no.2
• /
• pp.69-74
• /
• 2013

In this paper, we propose the pattern classification algorithm of recognizing wrist movements based on electromyogram(EMG) to raise the recognition rate. We consider 30 characteristics of EMG signals wirh the root mean square(RMS) and the difference absolute standard deviation value(DASDV) for the extraction of precise features from EMG signals. To get the groups of each wrist movement, we estimated 2-dimension features. On this basis, we divide each group into two parts with mean to compare and promote the recognition rate of pattern classification effectively. For the motion classification based on EMG, the k-nearest neighbor(k-NN) is used. In this paper, the recognition rate is 92.59% and 0.84% higher than the study before.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.5 no.1
• /
• pp.95-101
• /
• 2011

In this paper, we propose the gaussian mixture model based pattern classification algorithm of forearm electromyogram. We define the motion of 1-degree of freedom as holding and unfolding hand considering a daily life for patient with prosthetic hand. For the extraction of precise features from the EMG signals, we use the difference absolute mean value(DAMV) and the mean absolute value(MAV) to consider amplitude characteristic of EMG signals. We also propose the D_DAMV and D_MAV in order to classify the amplitude characteristic of EMG signals more precisely. In this paper, we implemented a test targeting four adult male and identified the accuracy of EMG pattern classification of two motions which are holding and unfolding hand.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.40 no.2
• /
• pp.9-16
• /
• 2003

We proposed a method of a virtual robot arm controlled by the EMG pattern recognition using an improved SOFM method. The proposed method is simple in that the EMG signals are used as SOFM's input directly without preprocessing but nevertheless input patterns are reliably classified and then used for fuzzy logic systems to automatically tune the neighborhood and the learning rate. In order to verify the effectiveness of the proposed method, we experimented on EMG pattern recognition of 6 movements from the shoulder, wrist, and elbow. Experimental results show that the proposed SOFM method has 21.7% higher recognition rate than the general SOFM method, the average number of learning iterations has been decreased, and then the virtual robot arm is controlled by EMG pattern recognition.

• 전기의세계
• /
• v.34 no.9
• /
• pp.553-561
• /
• 1985

지금까지 EMG신호를 이용한 보철제어의 역사적 고찰과 기술적인 고려사항, 실제적인 예, 그리고 전망등에 관하여 살펴보았는데 "근육이 세계를 움직인다."는 말처럼 근전도신호는 인간-기계 상호연결에 무한한 잠재성을 보여주기 때문에 앞으로 이분야에 많은 연구가 필요하리라 생각된다. 더우기, 앞으로 몇년안에는 근육전기 제어(Myoelectric control)외에 신경으로 부터 추출된 제어신호를 이용한 신경전기제어(Neuroelectric control)도 가능하리라 믿는다. 특히, 근전도 신호처리에 관한 연구결과는 실제로 로보트제어에 기여를 하고 있는데 그 예로 Saridis의 연구결과를 들 수 있겠다. 그러므로, 근전도 신호처리에 관한 연구는 산업용 로보트 개발에도 크게 도움이 될 것이다. 최근의 첨단과학 즉, 전자공학, 컴퓨터공학, 제어공학, 반도체공학, 기계공학, 생체공학을 위시한 각 기술분야의 급격한 진보와 생리학, 체육학등 기초만 아니라 "Bionic Person" 혹은 "Artificial Man"을 가능하게 할 것이다.ificial Man"을 가능하게 할 것이다. 것이다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.05a
• /
• pp.505-506
• /
• 2017

본 연구는 기존 재활치료전문가의 경험 기반 재활치료 훈련기법에서 진일보하여 보다 객관적인 데이터 기반의 효율적인 재활치료 지원이 가능한 보조시스템을 개발하고자 하였다. 근육의 움직임에 따른 활동전위를 측정하는 근전도와 실제 근육의 움직임에 따른 활동 상태를 가속도 및 자이로센서를 활용하여 측정함으로써 재활치료 시 보다 객관화된 데이터의 축적과 치료계획의 수립이 가능하다. 이를 위하여 본 연구에서는 재활치료 중 근전도 신호와 가속도, 자이로 센서를 결합한 재활운동효과 모니터링 시스템을 구현하였다. 그리고 구현된 시스템의 성능평가를 위해 피실험자 5명을 대상으로 다양한 재활치료 운동방법별 근전도 신호와 가속도 및 자이로센서 신호를 측정 및 분석하였다.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.240-244
• /
• 2008

This paper presents an EMG-based wireless and wearable computer interface. The wearable device contains 4 channel EMG sensors and is able to acquire EMG signals using signal processing. Obtained signals are transmitted to a host computer through wireless communication. EMG signals induced by the volitional movements are acquired from four sites in the lower limb to extract a user's intention and six classes of wrist movements are discriminated by employing an artificial neural network (ANN). This interface could provide an aid to the limb disabled to directly access to computers and network environments without conventional computer interface such as a keyboard and a mouse.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.8
• /
• pp.260-267
• /
• 2018

Surface electromyography (sEMG) is mainly used as an on/off switch in the early stage of the study and was then expanded to navigational control of powered-wheelchairs and recognition of sign language or finger gestures. There are difficulties in communication between people who know and do not know sign language; therefore, many efforts have been made to recognize sign language or finger gestures. Recently, use of sEMG signals to recognize sign language signals have been investigated; however, most studies of this topic conducted to date have focused on Chinese finger number gestures. Since sign language and finger gestures vary among regions, Korean- and Chinese-finger number gestures differ from each other. Accordingly, the recognition performance of Korean finger number gestures based on sEMG signals can be severely degraded if the same muscles are specified as for Chinese finger number gestures. However, few studies of Korean finger number gestures based on sEMG signals have been conducted. Thus, this study was conducted to identify potential forearm muscles from which to collect sEMG signals for Korean finger number gestures. To accomplish this, six Korean finger number gestures from number zero to five were investigated to determine the usefulness of the proposed muscles and electrode placements by showing that CNN technique based on sEMG signal after sufficient learning recognizes six Korean finger number gestures in accuracy of 100%.

• Journal of Biomedical Engineering Research
• /
• v.16 no.4
• /
• pp.507-516
• /
• 1995

The nonstationary identifier in the DCT domain is suggested in this study for the identification of AR parameters of above-lesion upper-trunk electromyographic (EMG) signals as a means of developing a reliable real time signal to control functional electrical stimulation (FES) in paraplegics to enable primitive walking. As paraplegic shifts his posture from one attitude to another, there is transition period where the signal is clearly nonstationary. Also as muscle fatigues, nonstationarities become more prevalent even during stable postures. So, it requires a develpment of time varying nonstationary EMG signal identifier. In this paper, time varying nonstationary EMG signals are transformed into DCT domain and the transformed EMG signals are modeled and analyzed in the transform domain. In the DCT domain, we verified reduction of condition number and increment of the smallest eigenvalue of input correlation matrix that influences numerical properties and mean square error were compared with SLS algorithm, and the proposed algorithm is implemented using IMS T-805 parallel processing computer for real time application.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2007.05a
• /
• pp.121-124
• /
• 2007

최근 각종 질환의 원인이 되고 있는 비만은 심각한 사회문제로 대두되고 있으며, 이를 해결하기 위해 비만관리를 위한 측정 시스템의 필요성이 증가하고 있다. 본 논문은 비만관리를 위해 복부의 근전도 신호를 분석해서 언제 어디서든 본인의 건강상태를 체크하여 적절한 의료 서비스를 받을 수 있는 측정 시스템에 관한 연구이다. 복부 근전도 신호 분석을 위해서 에너지 검출, 신호 특징 추출, 상태 분류 및 인식 등을 위한 알고리즘을 제안한다. 이 신호 분석 알고리즘을 측정 시스템에 적용하여 복부의 비만도 및 복부의 근력을 평가하여 건강상태에 대한 적절한 평가를 제공하는 시스템을 제안한다.