• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.55 no.7
• /
• pp.344-351
• /
• 2006

The fatigue of back muscle in the repetitive lifting motion was studied using multiple parameters(FFT_MDF, RMS, 2C, NT) in this study. Recent developments in the time-frequency analysis procedures to compute the IMDF(instantaneous median frequency) were utilized to overcome the nonstationarity of EMG signal using Cohen-Posch distribution. But the above method has a lot of computation time because of its complexity. So, in this study, FFT_MDF(median frequency estimation based on FFT) algorithm was used for median frequency estimation of back muscle EMG signal during muscle work in uniform velocity portion of lumbar movement. The analysis period of EMG signal was determined by using the run test and lumbar movement angle in dynamic task, such as lifting. Results showed that FFT_MDF algorithm is well suited for the estimation of back muscle fatigue from the view point of computation time. The negative slope of a regression line fitted to the median frequency values of back muscle EMG signal was taken as an indication of muscle fatigue. The slope of muscle fatigueness with FFT_MDF method shows the similarity of 77.8% comparing with CP_MDF(median frequency estimation based on Cohen Posch distribution) method.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.53 no.5
• /
• pp.380-388
• /
• 2004

This study proposed an improved spike detection algorithm which automatically detects suitable spike threshold on the amplitude of surface electromyography(SEMG) signal during isometric contraction. The EMG data from the low back muscles was obtained in six channels and the proposed signal processing algorithm is compared with the median frequency and Gabriel's spike parameter. As a result, the reliability of spike parameter was inferior to the median frequency. This fact indicates that a spike parameter is inadequate for analysis of multi-channel EMG signal. Because of uncertainty of fixed spike threshold, the improved spike detection algorithm was proposed. It automatically detects suitable spike threshold depending on the amplitude of the EMG signal, and the proposed algorithm was able to detect optimal threshold based on mCFAR(modified Constant False Alarm Rate) in the every EMG channel. In conclusion, from the reliability points of view, neither median frequency nor existing spike detection algorithm was superior to the proposed method.

• Journal of Biomedical Engineering Research
• /
• v.15 no.2
• /
• pp.167-174
• /
• 1994

In this paper, a DSP and microcomputer-based EMG controlled functional electrical stimulation (FES) system, for restoring walking of paraplegics at the patients' own command, is presented. The above-lesion EMG is a time-varying nonstationary signal and its autoregressive (AR) parameters are identified by the nonstationary identification algorithm using a DSP chip. The identified AR parameters are used for the cloassification of the function and the control of the movement. The below-lesion response-EMG signal is used as a measure of muscle fatigue. This FES system is designed to measure muscle fatigue and control the stimulation intensity according to the amplitude of the response-EMG signal. While the automatic electrical intensity control is obtained by identifying the movement, the proposed FES system is suitable for the automatic control of paraplegic walking.

• Journal of the Ergonomics Society of Korea
• /
• v.30 no.6
• /
• pp.689-694
• /
• 2011

Objective: The aim of this study is to evaluate the fluctuation of signal amplitude during repetitive dynamic contraction based on surface electromyography(EMG). Background: The most previous studies were considered isometric muscle contraction and they were difference to smoothing window length by moving average filter. In practical, the human movement is dynamic state. Dynamic EMG signal which indicated as the nonstationary pattern should be analyzed differently compared with the static EMG signal. Method: Ten male subjects participated in this experiment, and EMG signal was recorded by biceps brachii, anterior/posterior deltoid, and upper/lower trapezius muscles. The subject was performed to repetitive right horizontal lifting task during ten cycles. This study was considered three independent variables(muscle, amplitude processing technique, and smoothing window length) as the within-subject experimental design. This study was estimated muscular activation by means of the linear envelope technique(LE). The dependent variable was set coefficient of variation(CV) of LE for each cycle. Results: The ANOVA results showed that the main and interaction effects between the amplitude processing technique and smoothing window length were significant difference. The CV value of peak LE was higher than mean LE. According to increase the smoothing window length, this study shows that the CV trend of peak LE was decreased. However, the CV of mean LE was analyzed constant fluctuation trend regardless of the smoothing window length. Conclusion: Based on these results, we expected that using the mean LE and 300ms window length increased reproducibility and signal noise ratio during repetitive dynamic muscle contraction. Application: These results can be used to provide fundamental information for repetitive dynamic EMG signal processing.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.2209-2212
• /
• 2003

In this paper, we propose the method to extract the important frequency bands from the EMG signal, and for generation of feature vector using the important frequency bands. The EMG signal is measured with 4 sensor and is recorded as 4 channel’s time series data. The same frequency bands from 4 channel’s frequency components are selected as the important frequency bands. The feature vector is calculated by the function formed using the combination of selected same important frequency bands. The EMG signals acquired from seven wrist motion type are recognized by changing into the feature vector formed. Then, the extraction and generation is performed by using the double combination of the genetic algorithm (GA) and the neural network (NN). Finally, in order to illustrate the effectiveness of the proposed method, computer simulations are done.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.8
• /
• 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.

• Proceedings of the ESK Conference
• /
• 1997.04a
• /
• pp.107-113
• /
• 1997

In this paper, an interfacing method to control rehabilitation assitance system with bio-signal is proposed. Controlling with EMG signals method has certain advantage on signal-collecting, but has some drawbacks in the function resolution of EMG signals because data-processing process is not efficient. To improve function-resolution and to increase the efficiency of EMG signal interfacing with rehabilitation assistance system, Multi-layer Perception which is highly effective with static signal and hidden-Markov model for dynamic signal resolving are fused together. In proposed method. The direction and average speed of the rehabilitation assitance system are controlled by the trajectory control and estimation of the moving direction result from the fused model. From the experiment, proposed GMM and 2-level MLP hybrid-classifier yielded 8.6% perception-error rate, improving function resolution. New acceleration control method constructed with 3 nested linear filter produced continuous acceleration paths without the information of destination point. Thus, the mass output caused by non- continuous acceleration-deceleration was eliminated. In the simulation, the necessary calculation, in the case of multiplication, was reduced by 11.54%.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.22 no.5
• /
• pp.533-539
• /
• 2012

The purpose of this study is to estimate a validity of control signal through a design of Exoskeleton Robot Arm's capable of intelligent recognition as a human arm's motion by using realtime processed data of generated EMG signals. By an intelligent algorithm, the EMG output value of human biceps and triceps muscles contraction can be recognized and used for the control over exoskeleton arm corresponding to human's recognition and judgement. The EMG sensing data of muscles contraction and relaxation are used as the input signal from human's body to operate the Exoskeleton Robot Arm thus copying human arm motion. An intelligent control of Exoskeleton Robot Arm is to design the analog control circuit which processes the input data, and then to manufacture an integrated control board. And then abstracted signal is passed by DSP signal processing, Fuzzy logic algorithm is designed for a accurate prediction of weight or load through the intelligent algorithm, and design an Exoskeleton Robot Arm to express a human's intention.

• Proceedings of the IEEK Conference
• /
• 2003.07c
• /
• pp.2733-2736
• /
• 2003

This paper proposes a dry-type active surface EMG electrode for the myoelectric prosthetic hand. The designed electrode is small size for embedding in the socket of prosthetic hand, and it has three leads including the reference of signal. To acquire EMG signal rejected the power noise, a precision differential amplifier and various filters such as the band pass filter band rejection filter, low pass and high pass filter are embedded on the electrode. The final output of the electrode is integrated absolute EMG (IEMG) obtained by full rectifier and moving average circuits. From experimental results using the implemented dry-type active surface EMG electrode, the proposed electrode is feasible for the myoelectric prosthetic hand.

• Proceedings of the KOSOMBE Conference
• /
• v.1990 no.05
• /
• pp.73-78
• /
• 1990

In this study, LPC cepstrum coefficients are used as feature vector extracted from AR model of EMG signal, and a reduced-connection network which has reduced connection between nodes is constructed to classify and recognize EMG functional classes. The proposed network reduces learning time and improves system stability. Therefore it is shown that the proposed network is appropriate in recognizing the function of EMG signal.