• Physical Therapy Korea
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• v.29 no.2
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• pp.106-116
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• 2022

Background: Shoulder external rotation exercises are commonly used to improve the stabilizing ability of the infraspinatus. However, during exercise, excessive activation of the posterior deltoid compared to the infraspinatus causes the humeral head to move anteriorly in an abnormal position. Many researchers have emphasized selective activation of the infraspinatus during shoulder external rotation exercise. Objects: This study aims to delineate the optimal exercise method for selective activation of infraspinatus by investigating the muscle activities of the infraspinatus and posterior deltoid according to the four shoulder exercise methods and two forearm positions. Methods: Thirty healthy individuals participated in this study. The participants were instructed to perform shoulder external rotation exercises following four exercise methods: sitting external rotation (SIER); standing external rotation at 90° abduction (STER); prone external rotation at 90° abduction (PRER); side-lying external rotation (SLER), and two forearm positions (neutral, supinated). The electromyography (EMG) signal amplitude was measured during each exercise. Surface EMG signals were recorded from the posterior deltoid, infraspinatus, and biceps brachii. Results: EMG results of the infraspinatus and posterior deltoid in PRER, were significantly higher than that of the other exercises (p < 0.01). The EMG ratio (infraspinatus/posterior deltoid) in SIER was significantly higher than that of the other exercises. EMG activation of the posterior deltoid in SIER, PRER, and SLER was significantly higher in neutral than in supinated (p < 0.01). Furthermore, the EMG of the infraspinatus in SIER was significantly higher in neutral than in supinated (p < 0.01). The EMG ratio (infraspinatus/ posterior deltoid) in SIER was significantly higher in neutral than in supinated (p < 0.05.) Contrarily EMG ratios in PRER and SLER were significantly higher in supinated than in neutral (p < 0.05). Conclusion: The results show that clinicians should consider these exercise methods and forearm positions when planning shoulder external rotation exercises for optimal shoulder rehabilitation.

• Physical Therapy Korea
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• v.8 no.3
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• pp.63-76
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• 2001

Many studies have shown that the initial median frequency (MDF) and slope correlate with the muscle fiber composition. This study tested the hypothesis that the initial MDF and slope are fixed, regardless of the interval at which data are collected. MDF data using moving fast Fourier transformation of EMG signals, following local fatigue induced by isotonic exercise, were obtained. An inverse FFT was used to eliminate noise, and characteristic decreasing regression lines were obtained. The regression analysis was done in three different periods, the first one third, first half, and full period, looking at variance in the initial MDF, slope, and fatigue index. Data from surface EMG signals during fatiguing isotonic exercise of the biceps brachii and vastus lateralis in 20 normal subjects were collected. The loads tested were 30% and 60% maximum voluntary contraction (MVC) in the biceps brachii and 40% and 80% MVC in the vastus lateralis. The rate was 25 flexions per minute. There were no significant differences in the initial MDF or slope during the early or full periods of the regression, but there was a significant difference in the fatigue index. Therefore, to observe the change in the initial MDF and slope of the MDF regression line during isotonic exercise, this study suggest that only the early interval need to be observed.

• The Journal of Korea Robotics Society
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• v.15 no.1
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• pp.62-69
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• 2020

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.

• Korean Journal of Applied Biomechanics
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• v.23 no.3
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• pp.235-243
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• 2013

The purpose of this study was to analyse scientific according to period of rehabilitation training of ACL patients. ACL patients seven subjects participated in this study. Gait (1.58 m/sec) analysis was performed by using a 3-D Cinematography, a Zebris system and a electromyograph system. The data were analyzed by paired t-test. The joint angles were recorded from the ankle, knee, hip joints. Peak max dorsi-flexion and peak max plantar-flexion identified significant differences (p<0.05). Another angles were no significant difference. Vertical force (Fz) and max pressure variables improved 6 month RTP better than 3 month RTP. EMG were collected from 4 muscles (rectus femoris, biceps femoris, gastrocnemius, tibialis anterior) with surface electrides in gait system. EMG signals were rectified and smoothed data. EMG signas were no significant difference but they also improved 6 month RTP better than 3 month RTP. More research is necessary to determine exactly what constitutes optimal rehabilitation training period for ACL patients.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1641-1648
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• 2017

Many researchers had examined the validity of using the high-to-low ratio between two fixed frequency band amplitudes (H/L-FFB) from the surface electromyography of a face and body as the first spectral index to assess muscle fatigue. Despite these studies, the disadvantage of this index is the lack of a criterion for choosing the optimal border frequency. We tested the potential of using the high-to-low ratio between two signal spectral moments (H/L-SSM), without fixed border frequencies, to evaluate muscle fatigue and predict endurance time ($T_{end}$), which was determined when the subject was exhausted and could no longer follow the fixed contraction cycle. Ten healthy participants performed five sets of voluntary isotonic contractions until they could only produce 10% and 20% of their maximum voluntary contraction (MVC). The $T_{end}$ values for all participants were $138{\pm}35s$ at 10% MVC and $69{\pm}20s$ at 20% MVC. Changes in conventional spectral indices, such as the mean power frequency (MPF), Dimitrov spectral index (DSI), H/L-FFB, and H/L-SSM, were extracted from surface EMG signals and were monitored using the initial slope computed every 10% of $T_{end}$ as a statistical indicator and compared as a predictor of $T_{end}$. Significant correlations were found between $T_{end}$ and the initial H/L-SSM slope as computed over 30% of $T_{end}$. In conclusion, initial H/L-SSM slope can be used to describe changes in the spectral content of surface EMG signals and can be employed as a good predictor of $T_{end}$ compared to that of conventional spectral indices.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.49-55
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• 2002

A decrease in the birthrate and aging are progressing in Japan and several countries. In that society, it is important that physically weak persons such as elderly persons are able to take care of themselves. We have been developing exoskeletal robots for human (especially for physically weak persons) motion support. In this study, the controller controls the angular position and impedance of the exoskeltal robot system using multiple fuzzy-neuro controllers based on biological signals that reflect the human subject's intention. Skin surface electromyogram (EMG) signals and the generated wrist force by the human subject during the elbow motion have been used as input information of the controller. Since the activation level of working muscles tends to vary in accordance with the flexion angle of elbow, multiple fuzzy-neuro controllers are applied in the proposed method. The multiple fuzzy-neuro controllers are moderately switched in accordance with the elbow flexion angle. Because of the adaptation ability of the fuzzy-neuro controllers, the exoskeletal robot is flexible enough to deal with biological signal such as EMG. The experimental results show the effectiveness of the proposed controller.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.12 no.1
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• pp.16-26
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• 2006

The purpose of this study was to investigate the influence of the various knee angles and ground state on the muscular activities and fatigue of the ankle muscles by integrated electromyograms (iEMG) and median frequency of tibialis anterior (TA), peroneus longus (PL), flexor digitorum longus (FDL) and gastrocnemius (GC). Ten healthy male subjects were participated into stable and balance ball sessions at four angles of knee joint. The surface electromyograms (sEMG) were recorded from the TA, PL, FDL and GC on stable and balance ball with full weight bearing at four knee angles of $0^{\circ}$, $15^{\circ}$, $30^{\circ}$ and $45^{\circ}$. The time serial data of the surface electromyographic signals were transformed into integrated and frequency serial data by fast fourier transformation. On the stable ground, the iEMG signals of the TA, PL, FDL and GC were significantly higher at $45^{\circ}$ and $30^{\circ}$ of knee angles than $0^{\circ}$ and $15^{\circ}$ of knee flexion (p<0.05). On the balance ball, the iEMG of the TA, PL, FDL and GC were significantly higher at $45^{\circ}$ and $30^{\circ}$ of knee angles than $0^{\circ}$ and $15^{\circ}$ of knee flexion (p<0.05). The median frequency of the TA, PL, FDL and GC were significantly lower at $45^{\circ}$ and $30^{\circ}$ of knee angles than $0^{\circ}$ and $15^{\circ}$ of knee on the stable ground (p<0.05). On the balance ball, also the median frequency of the TA, PL, FDL and GC were significantly lower at $45^{\circ}$ and $30^{\circ}$ of knee angles than $0^{\circ}$ and $15^{\circ}$ of knee flexion (p<0.05). The iEMG of the TA, PL, FDL and GC were significantly higher on the balance ball at $0^{\circ}$, $15^{\circ}$, $30^{\circ}$ and $45^{\circ}$ of knee angles compared with stable ground. The median frequency of the TA, PL, FDL and GC were significantly lower on the balance ball at $0^{\circ}$, $15^{\circ}$, $30^{\circ}$ and $45^{\circ}$ of knee angles compared with stable ground. These results indicate that the ground conditions and angles of the knee joint involved to muscular activities and fatigue of ankles muscles, may performed at first on stable ground and then balance ball in order to $0^{\circ}$, $15^{\circ}$, $30^{\circ}$ and $45^{\circ}$ of knee flexion.

• Journal of the HCI Society of Korea
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• v.10 no.2
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• pp.21-26
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• 2015

NUI (Natural User Interface) system interprets the user's natural movement or the signals from human body to the machine. sEMG (surface electromyogram) can be observed when there is any effort in muscle even without actual movement, which is impossible with camera and accelerometer based NUI system. In sEMG based movement recognition system, the minimal number of electrodes is preferred to minimize the inconvenience. We analyzed the decrease in recognition accuracy as decreasing the number of electrodes. For the four kinds of movement intention without movement, extension (up), flexion (down), abduction (right), and adduction (left), the multilayer perceptron classifier was used with the features of RMS (Root Mean Square) from sEMG. The classification accuracy was 91.9% in four channels, 87.0% in three channels, and 78.9% in two channels. To increase the accuracy in two channels of sEMG, RMSs from previous time epoch (50-200 ms) were used in addition. With the RMSs from 150 ms, the accuracy was increased from 78.9% to 83.6%. The decrease in accuracy with minimal number of electrodes could be compensated partly by utilizing more features in previous RMSs.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.157-166
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• 1995

The Electroencephalogram (EEG) and evoked potential (EP) t;ave widely been used for study of brain functions. The EEG and EP signals acquired from multi-channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG). Since these artifact-affected EEG signals degrade EEG mapping, the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. In this paper a neural-network based classification is proposed to replace or to reduce human expert's efforts and time. From experiments, the neural-network based classification performs as good as human experts : variation of decisions between the neural network and human expert appears even smaller than that between human experts.

• Journal of IKEEE
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• v.17 no.4
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• pp.505-510
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• 2013

In this paper, a method of estimating hand and elbow movements using electrocorticogram (ECoG) signals is proposed. Using multiple channels, surface electromyogram (EMG) signals and ECoG signals were obtained from patients simultaneously. The estimated movements were those to close and then open the hand and those to bend the elbow inward. The patients were encouraged to perform the movements in accordance with their free will instead of after being induced by external stimuli. Surface EMG signals were used to find movement time points, and ECoG signals were used to estimate the movements. To extract the characteristics of the individual movements, the ECoG signals were divided into a total of six bands (the entire band and the ${\delta}$, ${\Theta}$, ${\alpha}$, ${\beta}$, and ${\gamma}$ bands) to obtain the information entropy, and the maximum likelihood estimation method was used to estimate the movements. The results of the experiment showed the performance averaged 74% when the ECoG of the gamma band was used, which was higher than that when other bands were used, and higher estimation success rates were shown in the gamma band than in other bands. The time of the movements was divided into three time sections based on movement time points, and the "before" section, which included the readiness potential, was compared with the "onset" section. In the "before" section and the "onset" section, estimation success rates were 66% and 65%, respectively, and thus it was determined that the readiness potential could be used.