• Journal of Acupuncture Research
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• v.31 no.2
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• pp.51-63
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• 2014

Background or Objectives : The purpose of this study is to measure surface Electromyography(sEMG) of facial muscles in normal person and to find method for standardizing of sEMG's value. Methods : We measured 3points on face, frontalis muscle($GB_{14}$), zygomaticus muscle($SI_{18}$), orbicularis oris muscle($LI_{19}$) of 40 normal person by sEMG. 40 normal person consist with two groups, each 20 male, 20 female. Average age of subject was $26.50{\pm}4.79$. SEMG instrument QEMG-4 XL was used. After training exercise of facial muscles, sEMG's root mean square value was measured once. Results : 1. In whole experimental group, frontalis muscle's both side average was $78.36{\pm}40.87$, zygomaticus muscle's both side average was $84.70{\pm}49.81$, orbicularis oris's both side average was $104.83{\pm}38.81$. 2. Left side of Frontalis muscle, both side of zygomaticus muscle are high marked in male than female in statistically. 3. In whole experimental group, average of ratio comparing smaller value with bigger value in difference between left side and right side was $19.60{\pm}12.88$ %. 4. Average of asymmetry index(AI) was $11.46{\pm}8.36$ %. orbicularis oris muscle's average of AI had least difference was $8.95{\pm}7.50$ %. zygomaticus muscle's average of AI had most difference was $13.95{\pm}8.90$ %. Conclusions : The result of this study could provide useful information of field of sEMG is used in oriental medicine treatment of facial muscles. To assess efficacy of treatment in facial muscles, we need to standardize facial muscle's sEMG values by using AI, ratio comparing values and etc.

• The Journal of Korea Robotics Society
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• v.14 no.3
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.10
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• pp.4629-4635
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• 2012

This study was aimed at the relationship between peak EMG amplitude on low back muscles acting on L5/S1 and load center of gravity, trunk lateral bending while lifting an object. Musculoskeletal disorders including low back pain can occur even when handling heavy objects only once as well as when doing non-heavy materials repeatedly. 11 male subjects with average 23 age were required to lift a 15.8kg object symmetrically three times. Peak EMG amplitudes on 6 muscles related with L5/S1 were recorded and analyzed. The lifting conditions consisted of lifting symmetric load with no trunk lateral bending, asymmetric load with no trunk lateral bending, and asymmetric load with trunk lateral bending to the load center of gravity within an object. The results showed that peak EMG amplitude on back muscles contralateral to load center of gravity was observed greater in comparison with the symmetric load. Also, in case of lifting asymmetric load the posture with trunk lateral bending increased peak EMG amplitude on muscles contralateral to load center of gravity more than with no trunk lateral bending. This research can be used as one administrative intervention in order to reduce the low back pain incidence with suggesting workers that they keep the trunk not bending to load center of gravity if possible when lifting a heavy asymmetric object.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.249-257
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• 2001

The voluntary contracted EMG signal of spinal cord injured patients is very small because the information from central nervous system is not sufficiently transmitted to $\alpha$ motor neuron or muscle fiber. Therefore the acquisited EMG signal from needle or surface electrodes can not be identified obvious voluntary contraction pattern by muscle movement. In this paper we propose the extraction technique of voluntary muscle contraction and relaxation pattern from EMG signal of spinal cord injured patient whose EMG signal is composed of the linear sum of mo색 unit action potentials with two noise sources, additive noise assumed to be white Gaussian noise and high frequency discharge assumed to be not motor unit action potential but impulsive noise. In order to eliminate impulsive noise and additive noise from voluntary contracted EMG signal, we use the FatBear filter which is a nonarithmetic piecewise constant filter, and multiscale nonlinear wavelet denoising processing, respectively. The proposed technique is applied to the EMG signal acquisited from transverse myelitis patients to extract voluntary muscle contraction pattern.

• Journal of Biomedical Engineering Research
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• v.27 no.5
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• pp.237-244
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• 2006

This study proposes an approach to the performance improvement of EMG(Electromyogram) pattern recognition. MFCC(Mel-Frequency Cepstral Coefficients)'s approach is molded after the characteristics of the human hearing organ. While it supplies the most typical feature in frequency domain, it should be reorganized to detect the features in EMG signal. And the dynamic aspects of EMG are important for a task, such as a continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most approaches. Thus, this paper proposes reorganized MFCC and HMM-GMM, which is adaptable for the dynamic features of the signal. Moreover, it requires an analysis on the most suitable system setting fur EMG pattern recognition. To meet the requirement, this study balanced the recognition-rate against the error-rates produced by the various settings when loaming based on the EMG data for each motion.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.199-202
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• 1996

In this paper, we suggested the combination of HMM(Hidden Markov Model) and MLP (Multi-Layer Perceptron) with GA(genetic algorithm) for a recognition of EMG signals. To describe EMG signal's dynamic properties, HMM algorithm was adapted and due to its outstanding abilities in static signal classification MLP was connected as a real processor. We also used GA( Genetic Algorithm) for improving MLP's learning rate. Experimental results showed that the suggested classifier gave higher EMG signal recognition rates with faster learning time than other one.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.213-220
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• 2024

Fall detection and prevention technologies play a pivotal role in ensuring the well-being of individuals, particularly those living independently, where falls can result in severe consequences. This paper addresses the challenge of accurate and quick fall detection by proposing a Bayesian probability-based measure applied to surface electromyography (sEMG) signals. The proposed algorithm based on a Bayesian filter that divides the sEMG signal into transient and steady states. The ratio of posterior probabilities, considering the inclusion or exclusion of the transient state, serves as a scale to gauge the dominance of the transient state in the current signal. Experimental results demonstrate that this approach enhances the accuracy and expedites the detection time compared to existing methods. The study suggests broader applications beyond fall detection, anticipating future research in diverse human-robot interface benefiting from the proposed methodology.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.15 no.1
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• pp.61-70
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• 2005

This study investigated the spinal loads(L5/S1 disc compression and shear forces) predicted from four biomechanical models: one EMG model and three optimization models. Three objective functions used in the optimization models were to miminize 1) the cubed muscle forces : MF3, 2) the cubed muscle stress : MS3, 3) maximum muscle intensity : MI. Twelve healthy male subjects participated in the isometric voluntary exertion tests to six directions : flexion/extension, left/right lateral bending, clockwise/ counterclockwise twist. EMG signals were measured from ten trunk muscles and spinal loads were assessed at 10, 20, 30, 40, 50, 60, 70, 80, 90%MVE(maximum voluntary exertion) in each direction. Three optimization models predicted lower L5/S1 disc compression forces than the EMG model, on average, by 31%(MF3), 27%(MS3), 8%(MI). Especially, in twist and extension, the differences were relatively large. Anterior-posterior shear forces predicted from optimization models were lower, on average, by 27%(MF3), 21%(MS3), 9%(MI) than by the EMG model, especially in flexion(MF3 : 45%, MS3 : 40%, MI : 35%). Lateral shear forces were predicted far less than anterior-posterior shear forces(total average = 124 N), and the optimization models predicted larger values than the EMG model on average. These results indicated that the optimization models could underestimate compression forces during twisting and extension, and anterior-posterior shear forces during flexion. Thus, future research should address the antagonistic coactivation, one major reason of the difference between optimization models and the EMG model, in the optimization models.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.8
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• pp.132-141
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• 2009

In this paper, we describe finger force estimation from surface electromyogram (sEMG) data for intuitive and delicate force control of robotic devices such as exoskeletons and robotic prostheses. Four myoelectric sites on the skin were found to offer favorable sEMG recording conditions. An artificial neural network (ANN) was implemented to map the sEMG to the force, and its structure was optimized to avoid both under- and over-fitting problems. The resulting network was tested using recorded sEMG signals from the selected myoelectric sites of three subjects in real-time. In addition, we discussed performance of force estimation results related to the length of the muscles. This work may prove useful in relaying natural and delicate commands to artificial devices that may be attached to the human body or deployed remotely.

• Journal of the Korea Convergence Society
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• v.13 no.5
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• pp.9-19
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• 2022

In this study, a systematic review was conducted to analyze the method of applying sEMG to evaluate the swallowing function of the elderly at each stage of swallowing, and to help objectively measure the swallowing stage of the older adults in clinical practice. From 2011 to 2021, 7 studies that met the selection criteria were selected using Pubmed, Scopus, and Web of Science (WoS). As a result of this study, the older adults and adults were divided into an experimental group and a control group and the swallowing phase was analyzed using sEMG only for the older adults. sEMG was used to evaluate swallowing in the oral and pharyngeal stages, and the sEMG attachment site was attached to the swallowing muscle involved in each stage. The collected sEMG data were filtered using a bandpass-filter and a notch-filter, and were analyzed using RMS, amplitude, and maximum spontaneous contraction. In this study, it was found that sEMG can be used as a tool to objectively and quantitatively evaluate the swallowing function in stages. Therefore, it is expected that this study will activate various studies that incorporate sEMG to evaluate the swallowing function in stages.