• Physical Therapy Korea
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• v.11 no.2
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• pp.35-45
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• 2004

The purpose of this study was to investigate the correlation and characteristics between electromyographic (EMG) activities of lower leg muscles and the posturographic assessment of static balance control in normal adults. Twenty-four young, healthy adults(12 males, 12 females) participated in the study. Center of pressure (COP) parameters were obtained using force platform as total path distance, total sway area, X mean frequency and Y mean frequency for 20 seconds in the following conditions: (1) comfortable standing with eyes opened or closed, (2) uncomfortable standing (feet together) with eyes opened or closed, (3) virtual moving surround delivered using Head mount display (HMD) with four different moving patterns. The virtual moving patterns consisted of close-far, superior-inferior tilting (pitch), right-left tilting (roll), and horizontal rotation (yaw) movements. Surface electromyographic activites were recorded on the tibialis anterior, peroneus longus, medial and lateral heads of gastrocnemius muscles under each condition. Correlation between the posturographic measures and EMG activities were evaluated. Total path distance and total sway area of COP were significantly increased during uncomfortable standing. EMG activity of tibialis anterior was significantly more during uncomfortable standing and virtual moving surround stimulation than during comfortable standing. Total path distance and sway area of COP during comfortable standing with closed eyes showed significant positive correlation with the EMG activities of the lateral head of gastrocnemius muscle. Total path distances and total sway area of COP during muscle. Total path distances and total sway area of COP during presentation of virtual moving surround also had significant positive correlations with EMG activities of the lateral head of gastrocnemius muscle under close-far movement.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.799-804
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• 1991

This paper is concerned with the artificial control of prosthetic devices using the electromyographic(EMG) activities of biceps and triceps in human subject during isometric contraction adjustments at the elbow. And it was analysised about recognition of EMG signals and dynamic characteristics at arm movements of human. For this study the error signal of autoregressive(AR) model were used to discriminate arm movement patterns of human. Interaction of dynamic characteristics (Position, Velocity, Acceleration) and EMG of biceps and triceps at arm movements of human was measured.

• Journal of Mechanical Science and Technology
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• v.14 no.3
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• pp.338-349
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• 2000

Neck muscle forces and spinal loads at the C4/5 level were estimated that result from isometric voluntary ramp efforts gradually developing to maximums in flexion, extension, left lateral bending and right lateral bending. Electromyographic (EMG) activities, a three-dimensional anatomic data of the neck and a hybrid model, EMG-assisted optimization (EMGAO) model, were used. The model computed the cervical loads at 25%,50%,75%, and 100% of peak moments. The highest model-predicted C4/5 joint compressive forces occurred during flexion; $361\;({\pm}164)\;N,\;811\;({\pm}288)\;N,\;1207\;({\pm}491)\;N\;and\;1674\;({\pm}319)\;N$ in 25%, 50%, 75% and 100% of peak moment respectively. Variations in load distribution among the agonistic muscles and co-contractions of antagonistic muscles were estimated during ramp efforts. Results suggest that higher C4/5 joint loads than previously reported are possible during isometric, voluntary muscle contractions. These higher physiological loads at C4/5 level must be considered possible during orthopedic reconstruction at this level.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.468-472
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• 1992

In this paper, we classified electromyographic(EMG) signal for prothesis control using neural network. For this study fast Fourier transform(FFT) with ensemble averaged spectrum is applied to two-channeI EMG signal for biceps and triceps. We used the three layer network. And a cumulative back-propagation algorithm is used for classification of six arm functions, flexion and extension of elbow and pronation and supination of the forearm and abduction and adduction of wrist.

• Journal of Electrical Engineering and information Science
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• v.2 no.6
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• pp.20-27
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• 1997

We present a method of electromyographic(EMG) pattern recognition to identify motion commands for the control of a prosthetic arm by evidence accumulation with multiple parameters. Integral absolute value, variance, autoregressive(AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of the EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

• Journal of Biomedical Engineering Research
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• v.29 no.1
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• pp.8-16
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• 2008

The aim of this study was to evaluate the motor recovery in 4 chronic hemiparetic patients with Fugl-Meyer (FM) and EMG characteristics before and after the training program. The training was performed at 1hr/day, 5days/week during 6 weeks in 4 chronic stroke patients. Electromyographic activities of the affected hand were recorded during isometric wrist flexion/ extension movements. In all patients, FM was significantly improved after the 6-week training. Onset/offset delay of muscle contraction significantly decreased in the affected wrist after the training. The co-contraction ratio of flexor/extensor muscles decreased significantly. Also, onset/offset delay of muscle contraction and co-contraction ratio correlates significantly with upper limb motor impairment and motor recovery. This EMG technique allows an objective evaluation of changes in muscle activity in post-stroke patients, providing easily measurable, quantitative indices of muscle characteristics.

• Physical Therapy Korea
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• v.22 no.4
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• pp.17-26
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• 2015

The purpose of this study was to investigate the effects of visual electromyography (EMG) biofeedback on the EMG activity of the lower trapezius (LT), serratus anterior (SA), and upper trapezius (UT) muscles, the LT/UT and SA/UT EMG activity ratios, and the scapular upward rotation angle during scapular posterior tilting exercise (SPTE). Twenty-four subjects with round-shoulder posture participated in this study. The EMG activities of the LT, SA, and UT were collected during SPTE both without and with visual EMG biofeedback. The scapular upward rotation angle was measured at the baseline, after SPTE without visual EMG biofeedback, and after SPTE with visual EMG biofeedback. The LT, SA, and UT EMG activities, and the LT/UT and SA/UT EMG activity ratios were analyzed by paired t-test. The scapular upward rotation angle was statistically analyzed using one-way repeated analysis of variance. If a significant difference was found, a Bonferroni correction was performed (p=.05/3=.017). The EMG activities of LT and SA significantly increased, and the EMG activity of UT significantly decreased during SPTE with visual EMG biofeedback compared to SPTE without visual EMG biofeedback (p<.05). In addition, the LT/UT and SA/UT EMG activity ratios significantly increased during SPTE with visual EMG biofeedback compared to SPTE without visual EMG biofeedback (p<.05). Significant increases were found in the scapular upward rotation angle after SPTE without and with visual EMG biofeedback compared to baseline (p<.017), and no significant differences were observed in the scapular upward rotation angle between SPTE without and with visual EMG biofeedback. In conclusion, SPTE using visual EMG biofeedback may be an effective method for increasing LT and SA activities while reducing UT activity.

• The Journal of Korean Physical Therapy
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• v.25 no.6
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• pp.411-416
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• 2013

Purpose: The purpose of the present study was to determine the effects of activation of gluteus maximus (Gmax) and abdominal muscle using EMG biofeedback on lumbosacral and tibiocalcaneal angles in standing position. Methods: Fourteen healthy subjects with normal feet participated in the present study. Electromyographic (EMG) biofeedback using visual cue was used to activate the external oblique (EO) and Gmax. The lumbosacral and tibiocalcalcaneal angles were measured by electronic goniometers. All the subjects were instructed to activate the Gmax and EO monitoring increasing amounts of the muscle activities in each muscle. The lumbosacral and tibiocalcaneal angles were collected in three trials during resting and activation of each muscle using EMG biofeedback in standing position. The mean value of three trials was used in the data analysis. A paired-t test was used to compare the lumbosacral and tibiocalcaneal angles between resting and activation of the Gmax and EO using EMG biofeedback. Results: The lumbosacral and tibiocalcaneal angles were significantly less in the resting compared to activation using EMG biofeedback (p<0.05). Conclusion: The activaition of Gmax and abdominal muscles using EMG biofeedback play role to control the pronation of subtalar joint during the weight-bearing.

• 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.

• Journal of Oral Medicine and Pain
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• v.15 no.1
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• pp.37-44
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• 1991

The author has synchronously recorded average electromyographic activities of temporalis anteriors and masseter muscles and the maximum bite force on the mandibular first molar on the prferred chewing side. These activities were recorded in order to study the EMG activity pattern of the working side and the balancing side to maximum bete force and functioning state of muscle in 30 patients with TMD and in 30 healthy subjects as controls. The results were as follows : 1. The maximum bite force on the mandibular first molar on the preferred chewing side was 20.63kg in TMD patients and 53.30kg in the healthy subjects(p<0.01). The maximum bite force in TMD patients was 38.7% of the healthy subjects. 2. The average electromyographic activities of temporalis anterioris and masseter muscles on the working side and the balancing side during maximum bite force were lower in TMD patients than in the healthy subjects(p<0.01). The average electromyographic activities of each muscle in TMD patients were 61.0%-62.8% of the healthy subjects. 3. The proportionalities of average electromyographic activities of temporalis anteriors and masseter muscles on the working side and the balancing side to maximum bite force were greater in TMD patients than in the healthy subjects(p<0.01). 4. Between the working side and the balancing side, the proportionality of average electromyographic activity of temporalis anterior to maximum bite force on the working healthy subjects (p<0.01). The proportionality of average electromyographic activity of working side and the balancing side in both groups (p<0.05).