인간의 수행 요소인 국소 근육의 피로는 만성적인 손상을 초래한다. 이 연구는 근전도의 진폭(amplitude)과 근력(muscle force)에서 피로의 영향을 WL과 NWL로 비교하여 조사하였다. 손목 굽힘근의 근력은 시간이 흐름에 따라 WL과 NWL 모두에서 감소되었다. 그러나, WL과 NWL는 시간 경과에 따른 근전도 진폭에서는 차이를 보였다. WL의 근전도 진폭에서는 운동 후 48분에 가장 높은 변화를 보였다. 이를 통하여 근전도의 진폭파 근력이

Back muscles play an important role in protecting the spine. Epidemiological studies have shown that loads imposed on the human spine during daily living play a significant role in the onset of low back pain. No previous study has attempted to correlate the response of the trunk musculature with the type of external load. The purpose of this study was to use surface electromyography (EMG) to quantify the relative demands placed on the back muscles while lifting loads in one hand. Forty asymptomatic, twenty year-old subjects stood while lifting loads of 10% of body weight(BW) unilaterally. All EMG data were normalized to a percentage of the EMG voltage produced during no-load standing(%EMG). Our major analysis involved a paired t-test for repeated measures. Of particular note was the fact that the ipsilateral 10% of BW condition produced statistically less % EMG change than did the contralateral 10% of the condition.

이 논문의 주목적은 정상인을 대상으로 각기 다른 3가지의 (체중의 1.5 %, 3.0 %, 9.0 %) 부하를 통해서 자세의 불균형을 유발시켰을 때 나타나는 postural movement patterns을 기술하기 위한 연구이다. 연구대상의 허리중심에 체중부하를 주어 균형이 뒤로 이동하게 하여, surface EMG(표면 근전도)를 통하여 Tibialis anterior(Ta), Gastrocnernius(Gc), Quadriceps femoris(Q

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.

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.

Surface electromyogram (sEMG), which is a bio-electrical signal originated from action potentials of nerves and muscle fibers activated by motor neurons, has been widely used for recognizing motion intention of robotic prosthesis for amputees because it enables a device to be operated intuitively by users without any artificial and additional work. In this paper, we propose a training-free unsupervised sEMG pattern recognition algorithm. It is useful for the gesture recognition for the amputees from whom we cannot achieve motion labels for the previous supervised pattern recognition algorithms. Using the proposed algorithm, we can classify the sEMG signals for gesture recognition and the calculated threshold probability value can be used as a sensitivity parameter for pattern registration. The proposed algorithm was verified by a case study of a patient with partial-hand amputation.

This paper proposes a method to simultaneously estimate two degrees of freedom in wrist forces (extension - flexion, adduction - abduction) and one degree of freedom in grasping forces using Electromyography (EMG) signals of the forearms. To correlate the EMG signals with the forces, we applied a multi - layer perceptron(MLP), which is a machine learning method, and used the characteristics of the muscles constituting the forearm to generate learning data. Through the experiments, the similarity between the MLP target value and the estimated value was investigated by applying the coefficient of determination (R2) and root mean square error (RMSE) to evaluate the performance of the proposed method. As a result, the R2values with respect to the wrist flexionextension, adduction - abduction and grasping forces were 0.79, 0.73 and 0.78 and RMSE were 0.12, 0.17, 0.13 respectively.

본 논문에서는 편마비 환자들의 재활 훈련을 돕기 위하여 시각적인 피드백과 동기 부여를 줄 수 있는 다채널 근전도 기반의 체감형 무선 게임 장치를 제안하였다. 게임 콘텐츠는 환자의 회복 상태에 따라 적용할 수 있도록 채널 별로 제작하였으며, 속도 및 난이도를 조절할 수 있게 제작되었다. 본 장치의 유효성 검증을 위하여 편마비 환자 7명을 대상으로 적용하여 설문 조사를 하였다. 조사 결과는 치료 효과에 대한 기대감이 가장 높은 점수(4.14±0.38)를 보여주었으며, 설문 문항 전체적으로 3점 이상의 결과를 보여주었다. 이 연구의 필요성과 개발된 장치의 만족감을 확인할 수 있었으며, 본 장치는 재활 환자들에게 게임을 통하여 재미있게 재활 운동을 할 기회를 제공할 수 있을 것이다.

본 논문에서는 생체 신호를 이용하는 체감형 게임 인터페이스 개발을 위하여 근전도 신호로부터 사용자의 동작 의도를 실시간으로 인식할 수 있는 장치를 개발하여 방향성을 필요로 하는 게임에 적용하였다. 근전도 신호를 획득하기 위한 장치는 4 채널로 이루어지며, 정의되는 손목동작으로는 Up, Right, Down, Left로 규정하였다. 각각의 동작으로부터 획득한 신호를 문턱치와 채널 간의 비교를 통하여 사용자의 의도를 인식하게 하였다. 방향성 분류 결과를 통하여 키보드의 방향키를 제어하고, 게임에 적용하게 된다. 개발된 장치는 재미와 흥미를 유도하여 효과적인 운동을 기대할 수 있으며, 상용화된 게임에도 적용할 수 있다.

Unlike robotic systems, humans excel at a variety of tasks by utilizing their intrinsic impedance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulator's human's superior motor skills in contact tacks.This paper develops a novel method for estimating and predicting the human joint impedance using the electromyogram(EMG)signals and limb position measurements. The EMG signal is the summation of MUAPs(motor unit action potentials). Determination of the relationship between the EMG signals and joint stiffness is difficult, due to irregularities and uncertainties of the EMG signals. In this research, an artificial neural network(ANN)model was developed to model the relation between the EMG and joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. The feasibility of the developed model was confirmed by experiments and simulations.

In this paper, the prototype of surface EMG (ElectroMyoGram) sensor is developed for the robotic rehabilitation applications, and the developed sensor is composed of the electrodes, analog signal amplifiers, analog filters, ADC (analog to digital converter), and DSP (digital signal processor) for coding the application example. Since the raw EMG signal is very low voltage, it is amplified by about one thousand times. The artifacts of amplified EMG signal are removed by using the band-pass filter. Also, the processed analog EMG signal is converted into the digital form by using ADC embedded in DSP. The developed sensor shows approximately the linear characteristics between the amplitude values of the sensor signals measured from the biceps brachii of human upper arm and the joint angles of human elbow. Finally, to show the performance of the developed EMG sensor, we suggest the application example about the real-time human elbow motion acquisition by using the developed sensor.