• Journal of the Ergonomics Society of Korea
• /
• v.13 no.2
• /
• pp.65-79
• /
• 1994

A biomechanical model of lower extremity in seated postures was developed to assess muscular activities of lower extremity involved in a variety of foot pedal operations. The model incorporated four rigid body segments with the twenty-four muscles to represent lower extremity. This study deals with quasi-static movement to investigate dymanic movement effect in seated foot operation. It is found that optimization method which has been used for modeling the articulated body segments does not predict the forces generated from biarticular muscles and antagonistic muscles reasonably. So, the revised nonlinear optimization scheme was employed to consider the synergistic effects of biarticular muscles and the antagonistic muscle effects from the stabilization of the joint. For the model validation, three male subjects performen the experiments in which EMG activities of the nine lower extremity muscles were measured. Predicted muscle forces were compared with the corresponding EMG amplitudes and it showed no statistical difference. For the selection of optimal seated posture, a physiological meaningful criterion for muscular load sharing developed.

• Journal of Biosystems Engineering
• /
• v.34 no.2
• /
• pp.127-132
• /
• 2009

In the working population, muscle fatigue and musculoskeletal discomfort are common, which, in the case of insufficient recovery may lead to musculoskeletal pain. Workers suffering from musculoskeletal pains need to be rehabilitated for recovery. Isokinetic testing has been used in physical strengthening, rehabilitation and post-operative orthopedic surgery. Frequency analysis of electromyography (EMG) signals using the mean frequency (MNF) has been widely used to characterize muscle fatigue. During isokinetic contractions, EMG signals present strong nonstationarities. Hilbert-Haung transform (HHT) and autoregressive (AR) model have been known more suitable than Fourier or wavelet transform for nonstationary signals. Moreover, several analyses have been performed within each active phase during isokinetic contractions. Thus, the aims of this study were i) to determine which one was better suitable for the analysis of MNF between HHT and AR model during repetitive maximum isokinetic extensions and ii) to investigate whether the analysis could be repeated for sequential fixed epoch lengths. Seven healthy volunteers (five males and two females) performed isokinetic knee extensions at $60^{\circ}/s$ and $240^{\circ}/s$ until 50% of the maximum peak torque was reached. Surface EMG signals were recorded from the rectus femoris of the right thigh. An algorithm detecting the onset and offset of EMG signals was applied to extract each active phase of the muscle. Following the results, slopes from the least-square error linear regression of MNF values showed that muscle fatigue of all subjects occurred. The AR model is better suited than HHT for estimating MNF from nonstationary EMG signals during isokinetic knee extensions. Moreover, the linear regression can be extracted from MNF values calculated by sequential fixed epoch lengths (p> 0.0I).

• Journal of Biomedical Engineering Research
• /
• v.20 no.3
• /
• pp.315-321
• /
• 1999

The purpose of this study is to develop a mathematical model for system identification in order to predIct muscle force based on eledromyographic signal. Therefore, a finding of the relalionship between characteristics of electromyographic signal and the corre spondng muscle force should be necessiiry through dynamic, joint model. To develop the dynamic joint model, the upper limb mcludmg the wrist and elbow joint has been considered. The kinematic and dynamic data, such as joint angular displacement, velocity, deceleration along with the moment of inertla, required to establish the dynamic model has been obtained by electrical flexible goniometer which has two degree-of-frcedoms. ln this model, muscle force can be predicted only electromyographs through the relationship between the integrated lorce and the mtegrated electromyographic signal over the duration of muscle contraclion in this study.

• Journal of the Korean Society for Precision Engineering
• /
• v.26 no.7
• /
• pp.142-149
• /
• 2009

The aim of this study was to identify both the mechanical and reflex properties associated with spasticity in hemiplegic patients. Ten hemiplegic patients were included in this study. Multiple pendulum tests were executed for each subject, and knee joint angle and EMG of Rectus Femoris muscle were measured. The neuromusculoskeletal system model was developed from generally accepted mechanism and identified through minimization of the error in the model-predicted pendulum trajectories. The identification was successful in terms of small error in simulated kinematics and high sensitivity and precision of simulated torque against EMG activity. The reflex threshold showed significant difference between different clinical scores (p<0.01) and significant negative correlation (r=-0.93) with the EMG duration. It is expected that the suggested method may help in understanding mechanisms underlying spasticity.

• Proceedings of the KSME Conference
• /
• 2000.04a
• /
• pp.268-273
• /
• 2000

Velocity-dependent increase in tonic stretch reflexes is one of the prominent characteristics of spasticity. It is very important to evaluate spasticity objectively and quantitatively before and after treatment for physicians. An accurate quantitative biomechanical evaluation for the spasticity which is caused by the disorder of central nervous system is made in this study. A sudden leg dropper which is designed to generate objective testing environment at every trial gives very effective environment for the test. Kinematic data are archived by the 3-dimensional motion analysis system($Elite^{(R)}$, B.T.S., Italy). Kinematic data are angle and angular velocity of lower limb joints, and length and lengthening velocity of lower limb muscle. A program is also developed to analyze the kinematic data of lower limb, contraction and relaxation length of muscles, and dynamic EMG data at the same tim. To evaluate spasticity quantitatively, total 31 parameters extracted from goniogram, EMG and muscle model are analyzed. Statistical analysis are made for bilateral correlations for all parameters. The described instrumentation and parameters to make quantitative and objective evaluation of spasticity shows good results.

• Journal of Biomedical Engineering Research
• /
• v.45 no.2
• /
• pp.66-74
• /
• 2024

This study analyzed the occurrence of abnormal muscle coactivations based on the assistance of upper limb weight during reaching task in stroke patients. Nine chronic stroke survivors with hemiplegia performed reaching tasks using a programmable haptic robot. Electromyography (EMG) coactivation levels in the upper limb muscles were analyzed using a linear model describing the activation levels of two muscles when the patient's upper limb weight was assisted at 0%, 25%, and 50%. As the upper limb weight assistance of the haptic robot decreased, the magnitude of the EMG signal in both the deltoid and biceps muscles increased simultaneously on both the paretic and non-paretic sides. However, no difference was found between the paretic and non-paretic sides when comparing the slope of the linear model describing the activation relationship between the deltoid and biceps. The aforementioned results suggest that in some stroke survivors, the deltoids, triceps, and biceps on the paretic side may not be abnormally coupled when supporting the upper limbs against gravity. Furthermore, these results suggest that the combination of haptic robots and EMG analysis might be utilized for evaluating abnormal coactivations in stroke patients.

• Journal of Korea Multimedia Society
• /
• v.22 no.12
• /
• pp.1491-1499
• /
• 2019

Insufficient sleep time and bad sleep quality causes many illnesses and it's research became more and more important. The most common method for measuring sleep quality is the polysomnography(PSG). The PSG is a test used to diagnose sleep disorders. The most common PSG data is obtained from the examiner, which attaches several sensors on a body and takes sleep overnight. However, most of the sleep stage classification in PSG are low accuracy of the classification. In this paper, we have studied algorithm for sleep level classification based on machine learning which can replace PSG. EEG, EOG, and EMG channel signals are studied and tested by using CNN algorithm. In order to compensate the performance, a mixed model using both CNN and DNN models is designed and tested for performance.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.7
• /
• pp.1010-1024
• /
• 1995

An intelligent trajectory control method that controls a direction and a average velocity for a prosthetic arm by force and direction estimations using EMG signals is proposed. 3 stage linear filters are used as a real time joint trajectory planner to minimize the impact to human body induced by arm motions and to reduce muscle fatigues. We use combination of MLP and fuzzy filter for a limb direction estimation and a time model of force for determining a cartesian trajectory control parameter. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. Simulation results of the proposed method show that the arm is effectively followed the desired trajectory by estimated foreces and directions. This method reduces the number of electrodes and attatched sites compared with the method using Hogan's impedance control.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.05
• /
• pp.398-401
• /
• 1997

The EMG signal of spinal cord injured patient is very feeble because that the information from central nervous system is not sufficiently transmitted to molter neuron or muscle fiber. Therefore the observer can not observe contraction and relaxation movement of muscle from the raw EMG signal. In this paper, we propose the muscle contraction and relaxation pattern analysis method of spinal cord injured patient whose EMG signal is composed of the sum of motor unit action potential train with additive white Gaussian noise and impulsive noise. From the EMG model, we denoise impulsive noise using median filter which is a kind of nonlinear filter and the output of median filter is transformed to wavelet transform domain for denoising additive white Gaussian noise using threshold level removal technique. As a result, we can obtain the clear contraction and relaxation pattern.

• Physical Therapy Korea
• /
• v.28 no.2
• /
• pp.123-131
• /
• 2021

Background: Only 2% of falls in older adults result in serious injuries (i.e., hip fracture). Therefore, it is important to differentiate injurious versus non-injurious falls, which is critical to develop effective interventions for injury prevention. Objects: The purpose of this study was to a. extract the best features of surface electromyography (sEMG) for classification of injurious falls, and b. find a best model provided by data mining techniques using the extracted features. Methods: Twenty young adults self-initiated falls and landed sideways. Falling trials were consisted of three initial fall directions (forward, sideways, or backward) and three knee positions at the time of hip impact (the impacting-side knee contacted the other knee ("knee together") or the mat ("knee on mat"), or neither the other knee nor the mat was contacted by the impacting-side knee ("free knee"). Falls involved "backward initial fall direction" or "free knee" were defined as "injurious falls" as suggested from previous studies. Nine features were extracted from sEMG signals of four hip muscles during a fall, including integral of absolute value (IAV), Wilson amplitude (WAMP), zero crossing (ZC), number of turns (NT), mean of amplitude (MA), root mean square (RMS), average amplitude change (AAC), difference absolute standard deviation value (DASDV). The decision tree and support vector machine (SVM) were used to classify the injurious falls. Results: For the initial fall direction, accuracy of the best model (SVM with a DASDV) was 48%. For the knee position, accuracy of the best model (SVM with an AAC) was 49%. Furthermore, there was no model that has sensitivity and specificity of 80% or greater. Conclusion: Our results suggest that the classification model built upon the sEMG features of the four hip muscles are not effective to classify injurious falls. Future studies should consider other data mining techniques with different muscles.