• 한국군사과학기술학회지
• /
• 제20권1호
• /
• pp.119-128
• /
• 2017

G-induced Loss of Consciousness(G-LOC) can be predicted by measuring Electromyogram(EMG) signals. Existing studies have mainly focused on specific body parts and lacked of consideration with quantitative EMG indices. The purpose of this study is to analyze the indices of EMG signals by human body parts for monitoring G-LOC condition. The data of seven EMG features such as Root Mean Square(RMS), Integrated Absolute Value(IAV), and Mean Absolute Value(MAV) for reflecting muscle contraction and Slope Sign Changes(SSC), Waveform Length (WL), Zero Crossing(ZC), and Median Frequency(MF) for representing muscle contraction and fatigue was retrieved from high G-training on a human centrifuge simulator. A total of 19 trainees out of 47 trainees of the Korean Air Force fell into G-LOC condition during the training in attaching EMG sensor to three body parts(neck, abdomen, calf). IAV, MAV, WL, and ZC under condition after G-LOC were decreased by 17 %, 17 %, 18 %, and 4 % comparing to those under condition before G-LOC respectively. Also, RMS, IAV, MAV, and WL in neck part under condition after G-LOC were higher than those under condition before G-LOC; while, those in abdomen and calf part lower. This study suggest that measurement of IAV and WL by attaching EMG sensor to calf part may be optimal for predicting G-LOC.

• 대한인간공학회지
• /
• 제27권2호
• /
• pp.9-14
• /
• 2008

Coherence can be used to evaluate the functional cortical connections between the motor cortex and muscle. This study is to find coherence between EEG (electroencephalogram) and EMG (electromyogram) evoked by movement of a hand. Seven healthy participants were asked to perform thirty repetitive movement of right hand for ten seconds with rest for ten seconds. Specific feature of EEG components has been extracted by ICA (independent component analysis) and coherence between EEG and EMG was analyzed from data measured EEG in five local areas around central part of head and EMG in flexer carpri radialis muscle during grabbing movement. Coherence between EEG and EMG was successfully obtained at 0.025 confidence limit during hand movement and showed significant difference between rest and movement at 13-18Hz.

• 로봇학회논문지
• /
• 제3권1호
• /
• pp.9-15
• /
• 2008

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 manipulators human''s superior motor skills in contact tasks. 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.

• 제어로봇시스템학회논문지
• /
• 제15권3호
• /
• pp.286-292
• /
• 2009

This paper proposes a knee-wearable robot system for assisting the muscle power of human knee by processing EMG (Electromyogram) signals. Although there are many muscles affecting the knee joint motion, the rectus femoris and biceps femoris among them play a core role in the extension and flexion motion, respectively, of the knee joint. The proposed knee-wearable robot system consists of three parts; the sensor for measuring and processing EMG signals, controller for estimating and applying the required knee torque, and actuator for driving the knee-wearable mechanism. Ultimately, we suggest the motion control method for knee-wearable robot system by processing the EMG signals of corresponding two muscles in this paper. Also, we show the effectiveness of the proposed knee-wearable robot system through the experimental results.

• 로봇학회논문지
• /
• 제14권3호
• /
• pp.211-220
• /
• 2019

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.

• 대한의용생체공학회:의공학회지
• /
• 제27권5호
• /
• pp.237-244
• /
• 2006

This study proposes an approach to the performance improvement of EMG(Electromyogram) pattern recognition. MFCC(Mel-Frequency Cepstral Coefficients)'s approach is molded after the characteristics of the human hearing organ. While it supplies the most typical feature in frequency domain, it should be reorganized to detect the features in EMG signal. And the dynamic aspects of EMG are important for a task, such as a continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most approaches. Thus, this paper proposes reorganized MFCC and HMM-GMM, which is adaptable for the dynamic features of the signal. Moreover, it requires an analysis on the most suitable system setting fur EMG pattern recognition. To meet the requirement, this study balanced the recognition-rate against the error-rates produced by the various settings when loaming based on the EMG data for each motion.

• 재활복지공학회논문지
• /
• 제6권2호
• /
• pp.43-48
• /
• 2012

본 논문에서는 Electromyogram(EMG) 신호와 허벅지 각도 측정 장치, 발바닥 저항 센서를 이용하여 보행의 단계를 판단하는 방법을 제시한다. 신호의 측정을 위하여 건강한 성인 남성 5명을 대상으로 실험을 실시하였고 정상 보행에서의 EMG, 허벅지 각도, 발바닥 저항 센서를 통한 변화를 측정 하였다. EMG 신호의 획득을 위하여 실험자의 대퇴 사두근, 대퇴 이두근, 전경골근, 장딴지근에 Ag/AgCl 표면 전극을 부착하였으며, 양측 발뒤꿈치와 앞꿈치에 저항센서를 부착 하였다. 허벅지 각도 측정 장치는 굴곡 25도, 신전 20도 까지 범위를 가지며 이를 통하여 허벅지의 각도를 측정 하였다. 실험 결과 보행 시 입각기와 유각기를 명확히 판단 할 수 있었으며 세부적으로 8단계의 보행 상태를 판단 할 수 있었다.

• 제어로봇시스템학회논문지
• /
• 제14권12호
• /
• pp.1278-1285
• /
• 2008

An optimization-based real-time joint angle extraction method of human elbow is proposed by processing the biomedical signal of surface EMG (electromyogram) measured at the center point of biceps brachii. The EMG signal is known as non-stationary (time-varying) signal, but we assume that it is quasi-stationary because a physical or physiological system has limitations in the rate at which it can change its characteristics. Based on the assumption, a pre-processing method to obtain pre-angle values from raw EMG signal is firstly suggested, and then an optimization method to minimize the error between the pre-angle and real joint angle is proposed in this paper. Finally, we suggest the experimental results showing the effectiveness of the proposed algorithm.

• 제어로봇시스템학회논문지
• /
• 제13권8호
• /
• pp.769-776
• /
• 2007

This paper proposes the human arm motion tracking algorithm based on the signal processing for surface EMG (electromyogram) sensors attached on both upper arm and shoulder. The signals acquired by using surface EMG sensors are processed with choosing the maximum in a short period, taking the absolute value, and filtering noises out with a low-pass filter. The processed signals are directly used for the motion generation of virtual arm in real time simulator. The virtual arm of simulator has two degrees of freedom and complies with the flexion and extension motions of elbow and shoulder. Also, we show the validity of the suggested algorithms through the experiments.

• 제어로봇시스템학회논문지
• /
• 제11권1호
• /
• pp.41-49
• /
• 2005

Electromyogram (EMG) signal generated by voluntary contraction of muscles is often used in rehabilitation devices because of its distinct output characteristics compared to other bio-signals. This paper proposes a novel EMG-based human-computer interface for electric-powered wheelchair users with motor disabilities by C4 or C5 spine cord injury. User's commands to control the electric-powered wheelchair are represented by shoulder elevation motions, which are recognized by comparing EMG signals acquired from the levator scapulae muscles with a preset double threshold value. The interface commands for controlling the electric-powered wheelchair consist of combinations of left-, right- and both-shoulders elevation motions. To achieve a real-time interface, we implement an EMG processing hardware composed of analog amplifiers, filters, a mean absolute value circuit and a high-speed microprocessor. The experimental results using an implemented real-time hardware and an electric-powered wheelchair showed that the EMG-based human-computer interface is feasible for the users with severe motor disabilities.