Direct Validation of Model-Predicted Muscle Forces in the Cat Hindlimb During Locomotion

后肢 步态 比目鱼肌 生物力学 步态分析 解剖 肌腱 生物医学工程 模拟 计算机科学 物理 骨骼肌 物理医学与康复 医学
作者
Derya Karabulut,Suzan Cansel Doğru,Yi‐Chung Lin,Marcus G. Pandy,Walter Herzog,Yunus Ziya Arslan
出处
期刊:Journal of biomechanical engineering [ASM International]
卷期号:142 (5) 被引量:11
标识
DOI:10.1115/1.4045660
摘要

Various methods are available for simulating the movement patterns of musculoskeletal systems and determining individual muscle forces, but the results obtained from these methods have not been rigorously validated against experiment. The aim of this study was to compare model predictions of muscle force derived for a cat hindlimb during locomotion against direct measurements of muscle force obtained in vivo. The cat hindlimb was represented as a 5-segment, 13-degrees-of-freedom (DOF), articulated linkage actuated by 25 Hill-type muscle-tendon units (MTUs). Individual muscle forces were determined by combining gait data with two widely used computational methods-static optimization and computed muscle control (CMC)-available in opensim, an open-source musculoskeletal modeling and simulation environment. The forces developed by the soleus, medial gastrocnemius (MG), and tibialis anterior muscles during free locomotion were measured using buckle transducers attached to the tendons. Muscle electromyographic activity and MTU length changes were also measured and compared against the corresponding data predicted by the model. Model-predicted muscle forces, activation levels, and MTU length changes were consistent with the corresponding quantities obtained from experiment. The calculated values of muscle force obtained from static optimization agreed more closely with experiment than those derived from CMC.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
1秒前
华仔应助wzwz采纳,获得10
1秒前
啊这发布了新的文献求助10
1秒前
星辰大海应助accept采纳,获得30
1秒前
1秒前
dhu_johnny发布了新的文献求助10
2秒前
张雯思发布了新的文献求助10
2秒前
学物理的平应助admds采纳,获得10
2秒前
2秒前
丘比特应助兴武采纳,获得10
2秒前
3秒前
xh完成签到 ,获得积分10
3秒前
3秒前
善学以致用应助人人采纳,获得10
3秒前
丘比特应助考拉采纳,获得10
4秒前
4秒前
王迪应助加油小白菜采纳,获得10
4秒前
科研通AI2S应助酷炫橘子采纳,获得10
5秒前
5秒前
科研通AI6.3应助机械师简采纳,获得30
5秒前
炸药发布了新的文献求助10
6秒前
美子完成签到,获得积分10
7秒前
7秒前
纯真的羽毛完成签到,获得积分10
7秒前
Aeeeeeeon发布了新的文献求助10
7秒前
酷波er应助杆杆采纳,获得10
7秒前
7秒前
8秒前
松风水月发布了新的文献求助10
8秒前
fanhongpeng完成签到 ,获得积分10
8秒前
无私傲云发布了新的文献求助10
8秒前
科研通AI6.2应助公西若剑采纳,获得10
8秒前
天真有邪发布了新的文献求助10
8秒前
8秒前
8秒前
所所应助kzy采纳,获得10
8秒前
wanci应助内向的小脑采纳,获得10
9秒前
9秒前
huahua诀绝子完成签到,获得积分10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Signals, Systems, and Signal Processing 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6017040
求助须知:如何正确求助?哪些是违规求助? 7600720
关于积分的说明 16154591
捐赠科研通 5164894
什么是DOI,文献DOI怎么找? 2764769
邀请新用户注册赠送积分活动 1745863
关于科研通互助平台的介绍 1635068