• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.23-29
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• 2023

In this paper, we introduce a method to obtain high-quality results at a low cost for simulating musculoskeletal characters based on data from the reference motion through motion capture on two-legged walking through reinforcement learning. We reset the motion data of the reference motion to allow the character model to perform, and then train the corresponding motion to be learned through reinforcement learning. We combine motion imitation of the reference model with minimal metabolic energy for the muscles to learn to allow the musculoskeletal model to perform two-legged walking in the desired direction. In this way, the musculoskeletal model can learn at a lower cost than conventional manually designed controllers and perform high-quality bipedal walking.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.54-64
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• 2005

The study investigated genetic algorithms for the optimal control model of maximum height vertical jumping. The model includes forward dynamic simulations by the neural excitation-control variables. Convergence of genetic algorithms is very slow. In this paper the micro genetic algorithm(micro-GA) was used to reduce the computation time. Then a near optimal solution from micro-GA was an initial solution for VF02, which is one of well-developed and proven nonlinear programming algorithms. This approach provided the successful optimal solution for maximum-height jumping without a reasonable initial guess.

• Journal of the KSME
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• v.53 no.8
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• pp.34-37
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• 2013

이 글에서는 인체 근골격 동역학 모델을 이용하여, 인체 관절 내부의 하중을 예측하고 관절 질환의 원인을 분석하는 방법에 대한 최근 연구를 소개하고자 한다.

• Journal of the KSME
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• v.53 no.8
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• pp.28-33
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• 2013

이 글에서는 생체 내 근력과 관절 모멘트, 관절 접촉역학 분석에 이용되는 근골격계 생체역학 모델을 소개하고 그 연구 동향에 대해 소개하고자 한다.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.293-301
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• 2001

A patient-specific musculoskeletal model, whose parameters can be identified noninvasively, was developed for the automatic generation of patient-specific stimulation pattern in FES. The musculotendon system was modeled as a torque-generator and all the passive systems of the musculotendon working at the same joint were included in the skeletal model. Through this, it became possible that the whole model to be identified by using the experimental joint torque or the joint angle trajectories. The model parameters were grouped as recruitment of muscle fibers, passive skeletal system, static and dynamic musculotendon systems, which were identified later in sequence. The parameters in each group were successfully estimated and the maximum normalized RMS errors in all the estimation process was 8%. The model predictions with estimated parameter values were in a good agreement with the experimental results for the sinusoidal, triangular and sawlike stimulation, where the normalized RMS error was less than 17%, Above results show that the suggested musculoskeletal model and its parameter estimation method is reliable.

• CDE review
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• v.18 no.2
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• pp.38-46
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• 2012

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.475-478
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• 2023

컴퓨터 단층촬영(CT)을 활용한 골격근 단면적은 근감소증과 관련된 기능을 평가하는 데 사용된다. 일반적인 근감소증 연구는 요추 3번의 골격근량을 주로 보지만 암 또는 폐절제술과의 상관관계를 예측하기 위한 다양한 연구에서는 흉추 4번, 7번, 8번, 10번, 12번 다양한 수준의 골격근량으로 연구를 진행하고 있음을 알 수 있다. 본 논문에서는 흉부와 복부 CT 영상에서 근감소증 진단을 위해서 흉추와 요추의 영역별 슬라이스를 검출하기 위해서 CNN 구조의 EfficientNetV2를 전이학습하여 인공지능 모듈을 개발하였다. 인공지능 모듈은 전체 흉부 및 복부 CT 영상에서 Cervical, T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12, L1, L2, L3, L4, L5, Sacral 총 19 클래스를 검출하도록 하였다. Test 데이터셋을 사용하여 Confusion Matrix와 Grad-CAM으로 모델의 정확도를 시각화하여 보였으며 검증으로 인공지능 모듈의 정확성을 측정하였다. 끝으로 우리가 개발한 다기관 공동연구 지원플랫폼에 적용하여 시각화된 결과를 보였다.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.11 s.176
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• pp.16-23
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• 2005

• Journal of Science Education
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• v.42 no.2
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• pp.95-105
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• 2018

The purpose of this study was to develop a digital textbook on 'structure and contraction mechanism of skeletal muscle' with the learning model for biomimicry-based convergence. The unit of 'structure and contraction mechanism of skeletal muscle' is a part of Life Science I in high school. The convergence learning model was designed with three phases of biomimicry-based convergence (Exploration-Design-Implementation) including 3D modeling & printing. The developed digital textbook was composed of 8 sessions which contains the following learning contents : Exploration of skeletal muscle, creative designing of skeletal muscle using sketch application and 3D modeling, convergent implementing of the designed using 3D printing, exploration of muscle contraction, creative designing of muscle contraction using sketch application and 3D modeling, and convergent implementing of the designed using 3D printing. Each session is also involved in the contents of gallery widgets, media widgets, keynote widgets, sketch widgets, the cloud, polling widgets, and review widgets for interactive and mobile learning. After administering the developed digital textbook to 20 high school students, it was shown a positive effectiveness on life science learning for high school students. Moreover, the digital textbook was evaluated as to promote student's abilities on creative designs and implementation related to biomimicry-based convergence. The digital textbook was also shown a favorable response on students' interest and self-directed learning on life science.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.2
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• pp.73-78
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• 2020

Musculoskeletal disease is often caused by sitting down for long period's time or by bad posture habits. In order to prevent musculoskeletal disease in daily life, it is the most important to correct the bad sitting posture to the right one through real-time monitoring. In this study, to detect the sitting information of user's without any constraints, we propose posture measurement system based on multi-channel pressure sensor and CNN model for classifying sitting posture types. The proposed CNN model can analyze 5 types of sitting postures based on sitting posture information. For the performance assessment of posture classification CNN model through field test, the accuracy, recall, precision, and F1 of the classification results were checked with 10 subjects. As the experiment results, 99.84% of accuracy, 99.6% of recall, 99.6% of precision, and 99.6% of F1 were verified.