• Title/Summary/Keyword: 근-골격 모델

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Dystrophin Degradation in Skeletal Muscles with Lipid Enrichment in Cattle (지방 침착률이 높은 식용소에서 나타난 골격근의 디스트로핀 소실)

  • Jeon, Sung-Hwan;Kim, Ah-Young;Lee, Eun-Mi;Lee, Eun-Joo;Hong, Il-Hwa;Hwang, Ok-Kyung;Jeong, Kyu-Shik
    • Journal of Life Science
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    • v.26 no.5
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    • pp.592-602
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    • 2016

  • This study investigated the muscular dystrophin levels in freely moving Australian cattle mainly fed grass, freely moving Korean cattle fed mainly a grain diet, and Korean cattle fed a grain diet but housed in a relatively limited space of a cow house. The total skeletal muscle specimens of 244 cattle were collected and immediately fixed in 10% neutral formalin. The same area was biopsied from the cattle in both countries. The findings showed that fatty infiltration is highly correlated with membrane-associated protein degradation in skeletal muscle, and that among several membrane-associated proteins, dystrophin showed the most significant reduction in expression in the cattle with fatty infiltration. Similarly, CD36 was more highly expressed in the cattle with fatty infiltration of skeletal muscle. Various breeding factors, such as oxidative stress; the presence of oxidized lipids in the diet; and environmental factors such as exercise, temperature and amount of time spent, may have critical effects on the degradation of normal cytoskeleton proteins, which are required for maintaining normal skeletal muscle architecture. Among the sarcolemma membrane-associated proteins, dystrophin is the most sensitive membrane protein that is involved muscular dystrophy and muscular degeneration. Thus, the present findings may be useful for studies on muscular dystrophy in humans or the pathogenesis of muscular diseases in animal models.

  • https://doi.org/10.5352/JLS.2016.26.5.592 인용 PDF KSCI KPUBS HTML

Implementation of CNN Model for Classification of Sitting Posture Based on Multiple Pressure Distribution (다중 압력분포 기반의 착석 자세 분류를 위한 CNN 모델 구현)

  • Seo, Ji-Yun;Noh, Yun-Hong;Jeong, Do-Un
    • 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.

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Fall Detection Based on 2-Stacked Bi-LSTM and Human-Skeleton Keypoints of RGBD Camera (RGBD 카메라 기반의 Human-Skeleton Keypoints와 2-Stacked Bi-LSTM 모델을 이용한 낙상 탐지)

  • Shin, Byung Geun;Kim, Uung Ho;Lee, Sang Woo;Yang, Jae Young;Kim, Wongyum
    • KIPS Transactions on Software and Data Engineering
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    • v.10 no.11
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    • pp.491-500
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    • 2021

  • In this study, we propose a method for detecting fall behavior using MS Kinect v2 RGBD Camera-based Human-Skeleton Keypoints and a 2-Stacked Bi-LSTM model. In previous studies, skeletal information was extracted from RGB images using a deep learning model such as OpenPose, and then recognition was performed using a recurrent neural network model such as LSTM and GRU. The proposed method receives skeletal information directly from the camera, extracts 2 time-series features of acceleration and distance, and then recognizes the fall behavior using the 2-Stacked Bi-LSTM model. The central joint was obtained for the major skeletons such as the shoulder, spine, and pelvis, and the movement acceleration and distance from the floor were proposed as features of the central joint. The extracted features were compared with models such as Stacked LSTM and Bi-LSTM, and improved detection performance compared to existing studies such as GRU and LSTM was demonstrated through experiments.

  • https://doi.org/10.3745/KTSDE.2021.10.11.491 인용 PDF KSCI

A Study on the Stress and Strain Analysis of Human Muscle Skeletal Model in Kendo Three Typical Attack Motions (세 가지 주요 검도 공격 동작에서의 근-골격계 응력과 번형률 해석에 관한 연구)

  • Lee, Jung-Hyun;Lee, Young-Shin
    • Journal of the Korean Society for Precision Engineering
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    • v.25 no.9
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    • pp.126-134
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    • 2008

  • Kendo is one of the popular sports in modem life. Head, wrist and thrust attack are the fast skill to get a score on a match. Human muscle skeletal model was developed for biomechanical study. The human model was consists with 19 bone-skeleton and 122 muscles. Muscle number of upper limb, trunk and lower limb part are 28, 60, 34 respectively. Bone was modeled with 3D beam element and muscle was modeled with spar element. For upper limb muscle modelling, rectus abdominis, trapezius, deltoideus, biceps brachii, triceps brachii muscle and other main muscles were considered. Lower limb muscle was modeled with gastrocenemius, gluteus maximus, gluteus medius and related muscles. The biomechanical stress and strain analysis of human muscle was conducted by proposed human bone-muscle finite element analysis model under head, wrist and thrust attack for kendo training.

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