• Journal of the Ergonomics Society of Korea
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• v.14 no.2
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• pp.1-14
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• 1995

Neuromuscular difference between normal subjects and low-back pain patients has been identified in terms of neural excitation signal measured by Electromyography (EMG) under the dynamic flexion/extension trunk motion. Ten healthy subjects and ten low-back pain patients were recruited for this study. New parameters and normalization technique were introduced to quantify the muscle excitation pattern among the flexor-extensor pairs of muscles : rectus abdominis (RA)-erector spinae (ES at L1 and L5 level), external oblique (EO)-internal oblique (IO), rectus femoris (quadricep : QUD)-biceps femoris( hamstring : HAM), and tibialis anterior (TA)-gastrocnemius (GAS). Results indicated that the temporal EMG pattern such as peak timing difference between the hip flexor (QUD) and extensor (HAM) and the duration of coexcitation between ES at L5 and RA muscle pairs showed a statistically significant difference between normal subjects and low-back pain patients. Improtantly, this study presented a new technique to identify the dynamic muscle excitation pattern that canb be least affected by EMG-length-velocity relationship. Further study can performed to validate this method for clinical application to quantitatively identify the low-back pain patients in the future.

• Journal of Biomedical Engineering Research
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• v.13 no.1
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• pp.9-18
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• 1992

This paper describes a quantitative analysis of electromyography (EMG) measured from seven subjects performing four seated dynamic tasks. EMG signals were mom- bored using 15 surface electrodes which were placed on selected trunk and lower extrmity muscles of the right side of the body. Each EMG signal was then processed through rectification, integration, and filtering. Based on the maximum level of the processed EMG, it was found that the trunk and ankle muscles play an important role on the postural control during the seated tasks.

• Journal of Sensor Science and Technology
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• v.12 no.2
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• pp.88-94
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• 2003

Electromyogram (EMG) is the bioelectric signal induced by motor nerves. Analyzing EMG with the movement produced by muscle contraction, we can provide input commands to a computer as a man-machine interface as well as can evaluate the patient's motional abnormality. In this paper, we developed an integrated miniaturized device which acquires and transmits the surface EMG of an interested muscle. Developed system measures $60{\times}40{\times}25mm$, weighs 100g. Using an amplifier circuitry on the electrodes and the radio frequency transmission, the developed system dispenses with the use of cables among the electrodes, amplifier, and the post processing system (personal computer). The wiring used in conventional systems can be obstacle for natural motion and source of motion artifacts. In results, the developed system improves not only the signal-to-noise ration in dynamic EMG measurement, but also the user convenience. We propose a new human-computer interface as well as a dynamic EMG measurement system as a possible application of the developed system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.103-109
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• 2012

The purpose of this paper is to suggest the strategical lifting postures able to alleviate imbalanced EMG amplitude leading to an increase in low back muscle fatigue while lifting asymmetric load dynamically. Eleven male subjects are required to lift symmetrically an external load with 15.8kg and load center of gravity (LCG) deviated 10cm to the right from the floor to the waist height at the speed of about 25cm/sec. The EMG amplitudes on bilateral low back muscles (Longissimus, Iliocostalis, and Multifidus) are recorded during 2sec and analyzed. Independent variables are trunk postures (No bending vs. Bending to the LCG) and feet placements (Parallel vs. Right foot in front of the other vs. Right foot behind the other). Dependent variables are EMG amplitude average on six muscles and the EMG amplitude difference between right and left muscle group. Results indicate the phenomenon showing an amplitude increase in the left muscle group is equal to an decrease in the right one is observed in dynamic as well as static lifts, bending the trunk to the LCG increases amplitude discrepancy more than no trunk bending, and the amplitude discrepancy in one foot ipsilateral to LCG in front of the other foot is lowest among other foot postures. As bilateral EMG amplitude discrepancy increases total low back muscle fatigue, the strategical combination of no trunk bending and one foot close to LCG in front of the other is recommended for preventing elevated incidence of low back pain (LBP).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.5
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• pp.338-347
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• 2005

A computer-based low back muscle evaluation system was designed to simultaneously acquire, process, display, quantify, and correlate electromyographic(EMG) activity with muscle force, and range of motion(ROM) in the lumbar muscle of human. This integrated multi-channel system was designed around notebook PC. Each channel consisted of a time and frequency domain block, and T-F(time-frequency) domain block. The captured data in each channel was used to display and Quantify : raw EMG, histogram, zero crossing, turn, RMS(root mean square), variance, mean, power spectrum, median frequency, mean frequency, wavelet transform, Wigner-Ville distribution, Choi-Williams distribution, and Cohen-Posch distribution. To evaluate the performance of the designed system, the static and dynamic contraction experiments from lumbar(waist) level of human were done. The experiment performed in five subjects, and various parameters were tested and compared. This system could equally well be modified to allow acquisition, processing, and analysis of EMG signals in other studies and applications.

• Journal of Biomedical Engineering Research
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• v.31 no.4
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• pp.310-315
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• 2010

Estimation of muscle forces is important in biomechanics, therefore many researchers have tried to build a muscle model. Recently, optimization techniques for adjusting muscle parameters, i.e. EMG-driven model, have been used to estimate muscle forces and predict joint moments. In this study, an EMG-driven model based on the previous studies has been developed and isometric and isokinetic contraction movements were evaluated to validate the developed model. One healthy male participated in this study. The dynamometer tasks were performed for maximum voluntary isometric contractions (MVIC) for ankle dorsi/plantarflexors, isokinetic contraction at both $30^{\circ}/s$ and $60^{\circ}/s$. EMGs were recorded from the tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis and soleus muscles at the sampling rate of 1000 Hz. The MVIC trial was used to customize the EMG-driven model to the specific subject. Once the subject's own model was developed, the model was used to predict the ankle joint moment for the other two dynamic movements. When no optimization was applied to characterize the muscle parameters, weak correlations were observed between the model prediction and the measured joint moment with large RMS error over 100% (r = 0.468 (123%) and r = 0.060 (159%) in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). However, once optimization was applied to adjust the muscle parameters, the predicted joint moment was highly similar to the measured joint moment with relatively small RMS error below 40% (r = 0.955 (21%) and r = 0.819 (36%) and in $30^{\circ}/s$ and $60^{\circ}/s$ dynamic movements, respectively). We expect that our EMG-driven model will be employed in our future efforts to estimate muscle forces of the elderly.

• Journal of Biomedical Engineering Research
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• v.17 no.3
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• pp.379-386
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• 1996

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) and hidden Markov models (HMM's) hybrid classifier. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or vari- ous time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrasts the multilayer feedforward networks are suitable for static patterns. Ank a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of MLP and HMM algorithms that might lead to further improved EMG recognition systems.

• The Journal of Korean Physical Therapy
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• v.25 no.2
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• pp.81-87
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• 2013

Purpose: The purpose of the current study was to examine the effects of electromyography (EMG)-biofeedback training on lower extremity muscle activity and balance of patients with total knee replacement (TKR). Methods: Subjects were randomly allocated to two groups: experimental and control group. Subjects in the experimental group (n=10) were provided with quadriceps setting exercise by EMG-biofeedback (QSE+BF) and those in the control group were provided with QSE. Subjects in both groups were provided with the respective training programs for 20 minutes per session, five times per week, for a period of six weeks. To test significance, data analysis was performed using repeated-ANOVAs. Results: Statistically significant differences in muscle activity of the rectus femoris muscle and the vastus lateralis, and dynamic balance ability were observed in the experimental group, compared with the control group. In comparison of the muscle activity of the rectus femoris muscle and the vastus lateralis, and dynamic balance ability between different training periods within the groups, both groups showed statistically significant differences. Conclusion: EMG-biofeedback training is effective in improving lower extremity muscle activity and balance ability of patients with TKR, and should be effective in patients with other diseases.

• Journal of the Ergonomics Society of Korea
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• v.19 no.2
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• pp.87-99
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• 2000

This study aims to examine a fuzzy logic-based human expert EMG prediction model (FLHEPM) for predicting electromyographic responses of trunk muscles due to manual lifting based on two task (control) variables. The FLHEPM utilizes two variables as inputs and ten muscle activities as outputs. As the results, the lifting task variables could be represented with the fuzzy membership functions. This provides flexibility to combine different scales of model variables in order to design the EMG prediction system. In model development, it was possible to generate the initial fuzzy rules using the neural network, but not all the rules were appropriate (87% correct ratio). With regard to the model precision, the EMG signals could be predicted with reasonable accuracy that the model shows mean absolute error of 8.43% ranging from 4.97% to 13.16% and mean absolute difference of 6.4% ranging from 2.88% to 11.59%. However, the model prediction accuracy is limited by use of only two task variables which were available for this study (out of five proposed task variables). Ultimately, the neuro-fuzzy approach utilizing all five variables to predict either the EMG activities or the spinal loading due to dynamic lifting tasks should be developed.

• Physical Therapy Korea
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• v.17 no.4
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• pp.26-34
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• 2010

The purpose of this study was to compare the trunk and lower extremity muscle activity induced by six different conditions floor, intensity 0, 1, 3, 5 of whole body vibration (WBV), and Swiss ball during bridging exercise. Surface electromyography (EMG) was used to measure trunk and lower extremity muscles activity. Ten elderly women were recruited from Hong-sung Senior Citizen Welfare Center. The collected EMG data were normalized using reference contraction (during floor bridging) and expressed as a percentage of reference voluntary contraction (%RVC). To analyze the differences in EMG data, the repeated one-way analysis of variance was used. A Bonferroni's correction was used for multiple comparisons. The study showed that EMG activity of the rectus abdominis, external oblique, internal oblique, erector spinae and rectus abdominis muscles were not significantly different between six different conditions of during bridging exercise (p>.05). However, there were significantly increased EMG activity of the rectus femoris (p=.034) in the WBV intensity 0, 1, 3, and 5 conditions compared with the floor bridging condition. EMG activity of the medial gastrocnemius were significantly increased in the WBV intensity 0, 1, 3, 5 and Swiss ball conditions compared with the floor bridging condition. Future studies are required the dynamic instability condition such as one leg lifting in bridging.