• Journal of Korean Institute of Industrial Engineers
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• v.6 no.1
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• pp.27-38
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• 1980

This paper presents the validation of a stochastic model of muscle fatigue during static muscle contractions. Forty four laboratory experiments, covering eleven test conditions for two trained subjects, were run in order to estimate fatigue and recovery rates, based on EMG observations. The validation of the model was made by comparing the model predictions to the experimental fatigue time. The validation study supports that the stochastic model of muscle fatigue accurately represents the underlying fatigue process. The study also provides support that the fatigue model can be used as a monitor of individual muscle capabilities.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.11
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• pp.1476-1478
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• 1999

A musculotendon model is proposed to predict muscle force during muscle fatigue due to the continuous functional electrical stimulation(FES). Muscle fatigue dynamics can be modeled as the electrical admittance of muscle fibers and included in activation dynamics based on the{{{{ { Ca}^{2+ } }}}} kinetics. The admittance depends on the fatigue variable that monotonically increase or decrease if electrical pulse exists or not, and on the stimulation parameters and the number of applied pulses. In the response of the change in activation the normalized Hill-type contraction dynamics connected with activation dynamics decline the muscle shortening velocity and thus its force under muscle fatigue. The computer simulation shows that the proposed model can express the muscle fatigue and its recovery without changing any stimulation parameters.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.1029-1032
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• 1999

Muscle based face image synthesis is one of the most realistic approach to realize life-like agent in computer. Facial muscle model is composed of facial tissue elements and muscles. In this model, forces are calculated effecting facial tissue element by contraction of each muscle strength, so the combination of each muscle parameter decide a specific facial expression. Now each muscle parameter is decided on trial and error procedure comparing the sample photograph and generated image using our Muscle-Editor to generate a specific face image. In this paper, we propose the strategy of automatic estimation of facial muscle parameters from 2D marker movement using neural network. This also 3D motion estimation from 2D point or flow information in captered image under restriction of physics based face model.

• Journal of Korean Institute of Industrial Engineers
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• v.5 no.2
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• pp.21-36
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• 1979

Static muscle contractions when prolonged or frequently repeated result in discomfort, fatigue, and musculosketal injuries. An analytic and quantitative model has been developed in order to expand the working knowledge on muscle fatigue. In this paper, three Markov models of muscle fatigue are developed. These models are based on motor unit fatigue-recovery characteristics obtained from information on motor unit behavior as it relates to fatigue and graded exertions. Three successively more realistic models are developed that involve: (1) homogeneous motor units with intensity-dependent fatigue rates and state-independent recovery rates (the HMSI model); (2) homogeneous motor units, intensity-dependent fatigue rates and state-dependent recovery rates (the HMSD model); and (3) non-homogeneous motor units (i.e., Type S and Type F), intensity-dependent fatigue rates and state-dependent recovery rates (the HMSD model). The result indicate that a simple stochastic model provide a means to analyze the complex nature of muscle fatigue in sequential static exertions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.2
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• pp.322-327
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• 2003

Based on dynamic muscle model to construct efficient face animation in this paper 30 face modelling techniques propose. Composed face muscle by faceline that connect 256 point and this point based on dynamic muscle model, and constructed wireframe because using this. After compose standard model who use wireframe, because using front side and side 2D picture, enforce texture mapping and created 3D individual face model. Used front side of characteristic points and side part for correct mapping, after make face that have texture coordinates using 2D coordinate of front side image and front side characteristic points, constructed face that have texture coordinates using 2D coordinate of side image and side characteristic points.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.6
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• pp.122-130
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• 2009

The analysis of human movement requires the knowledge of the Hill type muscle parameters, the muscle-tendon and moment arm length change as a function of joint angles. However, values of a subject's muscle parameters are very difficult to identify. It turns out from a sensitivity analysis that the tendon slack length and maximum muscle force are the two critical parameters among the Hill-type muscle model. Therefore, it could be claimed that the variation of the tendon slack length and maximum muscle force from the Delp's reference data will change the muscle characteristics of a subject remarkably. A numeric optimization method to search these tendon parameters specific to a subject is proposed, and the accuracy of the developed algorithm is evaluated through a numerical simulation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.33-38
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• 1999

Muscle based face image synthesis is one of the most realistic approach to realize life-like agent in computer. Facial muscle model is composed of facial tissue elements and muscles. In this model, forces are calculated effecting facial tissue element by contraction of each muscle strength, so the combination of each muscle parameter decide a specific facial expression. Now each muscle parameter is decided on trial and error procedure comparing the sample photograph and generated image using our Muscle-Editor to generate a specific race image. In this paper, we propose the strategy of automatic estimation of facial muscle parameters from 2D marker movement using neural network. This also 3D motion estimation from 2D point or flow information in captered image under restriction of physics based fare model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.932-938
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• 2012

Facial expression has its significance in mutual communication. It is the only means to express human's countless inner feelings better than the diverse languages human use. This paper suggests muscle model-based 3D facial expression generation system to produce easy and natural facial expressions. Based on Waters' muscle model, it adds and used necessary muscles to produce natural facial expressions. Also, among the complex elements to produce expressions, it focuses on core, feature elements of a face such as eyebrows, eyes, nose, mouth, and cheeks and uses facial muscles and muscle vectors to do the grouping of facial muscles connected anatomically. By simplifying and reconstructing AU, the basic nuit of facial expression changes, it generates easy and natural facial expressions.

• 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 Power System Engineering
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• v.10 no.3
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• pp.97-103
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• 2006

In this study, a position trajectory tracking control algorithm is proposed for a pneumatic artificial muscle driving apparatus composed of a actuator which imitates the muscle of human, a position sensor and a control valve. The controller applied to the driving apparatus is composed of a state feedback controller and disturbance observer. The feedback controller which feeds back position, velocity and acceleration is derived from the linear model of pneumatic artificial muscle driving apparatus. The disturbance observer is designed to improve trajectory tracking performance and to reduce the effect of model discrepancy. The effectiveness of the designed controller is proved by experiments and the experimental results show that the pneumatic artificial muscle driving apparatus with the proposed control algorithm tracks given position reference inputs accurately.