使用OpenSim进行下肢应用的实时逆运动学和逆动力学
Real-time inverse kinematics and inverse dynamics for lower limb applications using OpenSim.
作者信息
Pizzolato C, Reggiani M, Modenese L, Lloyd D G
机构信息
a School of Allied Health Sciences and Menzies Health Institute Queensland , Griffith University , Gold Coast , Australia.
b Department of Management and Engineering , University of Padua , Vicenza , Italy.
出版信息
Comput Methods Biomech Biomed Engin. 2017 Mar;20(4):436-445. doi: 10.1080/10255842.2016.1240789. Epub 2016 Oct 10.
Abstract
Real-time estimation of joint angles and moments can be used for rapid evaluation in clinical, sport, and rehabilitation contexts. However, real-time calculation of kinematics and kinetics is currently based on approximate solutions or generic anatomical models. We present a real-time system based on OpenSim solving inverse kinematics and dynamics without simplifications at 2000 frame per seconds with less than 31.5 ms of delay. We describe the software architecture, sensitivity analyses to minimise delays and errors, and compare offline and real-time results. This system has the potential to strongly impact current rehabilitation practices enabling the use of personalised musculoskeletal models in real-time.
摘要
关节角度和力矩的实时估计可用于临床、运动和康复场景中的快速评估。然而，目前运动学和动力学的实时计算基于近似解或通用解剖模型。我们提出了一种基于OpenSim的实时系统，该系统可在不进行简化的情况下求解逆运动学和动力学，每秒2000帧，延迟小于31.5毫秒。我们描述了软件架构、用于最小化延迟和误差的敏感性分析，并比较了离线和实时结果。该系统有可能对当前的康复实践产生重大影响，使个性化肌肉骨骼模型的实时使用成为可能。
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MOtoNMS: A MATLAB toolbox to process motion data for neuromusculoskeletal modeling and simulation.MOtoNMS：一个用于处理运动数据以进行神经肌肉骨骼建模和模拟的MATLAB工具箱。
Source Code Biol Med. 2015 Nov 16;10:12. doi: 10.1186/s13029-015-0044-4. eCollection 2015.
2
CEINMS: A toolbox to investigate the influence of different neural control solutions on the prediction of muscle excitation and joint moments during dynamic motor tasks.CEINMS：一个用于研究不同神经控制解决方案对动态运动任务期间肌肉兴奋和关节力矩预测影响的工具箱。
J Biomech. 2015 Nov 5;48(14):3929-36. doi: 10.1016/j.jbiomech.2015.09.021. Epub 2015 Oct 19.
3
Simbody: multibody dynamics for biomedical research.Simbody：用于生物医学研究的多体动力学
Procedia IUTAM. 2011;2:241-261. doi: 10.1016/j.piutam.2011.04.023.
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J Biomech. 2015 Feb 26;48(4):644-650. doi: 10.1016/j.jbiomech.2014.12.049. Epub 2015 Jan 5.
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6
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8
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J Biomech. 2013 Nov 15;46(16):2778-86. doi: 10.1016/j.jbiomech.2013.09.005. Epub 2013 Sep 12.
9
A real-time system for biomechanical analysis of human movement and muscle function.人体运动和肌肉功能的生物力学分析实时系统。
Med Biol Eng Comput. 2013 Oct;51(10):1069-77. doi: 10.1007/s11517-013-1076-z. Epub 2013 Jul 25.
10
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