Predictive simulation generates human adaptations during loaded and inclined walking.
Predictive simulation generates human adaptations during loaded and inclined walking.
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DOI：
10.1371/journal.pone.0121407
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发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Delp SL
中科院分区：
文献类型：
--
作者：
Dorn TW;Wang JM;Hicks JL;Delp SL
预测仿真是分析人体运动的一种有效方法。与跟踪实验数据的技术不同，预测模拟通过最小化代谢能量消耗等高级目标来合成步态，同时满足实现目标速度等任务要求。预测步态模拟的保真度仅针对平地上的运动数据进行了系统评估。在这项研究中，我们构建了一个基于能量最小化的预测模拟框架，并使用它来生成正常的步行，沿着与步行的范围进行负载和一系列的倾斜。模拟是肌肉驱动的，包括基于肌肉力量和伸展反射和腿部接触状态的控制器。我们展示了如何从适应模型的一系列环境变化中出现类似人类的运动策略。我们的模拟动力学不仅表现出良好的协议与实验数据正常行走在平地上（92%的关节角度轨迹和78%的关节扭矩轨迹位于1个标准偏差的实验数据），但也重现了许多显着的变化，关节角度，关节力矩，肌肉协调，和代谢能量消耗的实验研究中观察到的负载和倾斜行走。
Predictive simulation is a powerful approach for analyzing human locomotion. Unlike techniques that track experimental data, predictive simulations synthesize gaits by minimizing a high-level objective such as metabolic energy expenditure while satisfying task requirements like achieving a target velocity. The fidelity of predictive gait simulations has only been systematically evaluated for locomotion data on flat ground. In this study, we construct a predictive simulation framework based on energy minimization and use it to generate normal walking, along with walking with a range of carried loads and up a range of inclines. The simulation is muscle-driven and includes controllers based on muscle force and stretch reflexes and contact state of the legs. We demonstrate how human-like locomotor strategies emerge from adapting the model to a range of environmental changes. Our simulation dynamics not only show good agreement with experimental data for normal walking on flat ground (92% of joint angle trajectories and 78% of joint torque trajectories lie within 1 standard deviation of experimental data), but also reproduce many of the salient changes in joint angles, joint moments, muscle coordination, and metabolic energy expenditure observed in experimental studies of loaded and inclined walking.
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影响因子：
2.8
作者：
DeMers, Matthew S.;Pal, Saikat;Delp, Scott L.
通讯作者：
Delp, Scott L.
影响因子：
2.9
作者：
Dzeladini F;van den Kieboom J;Ijspeert A
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Ijspeert A
影响因子：
2.4
作者：
Liu, May Q.;Anderson, Frank C.;Schwartz, Michael H.;Delp, Scott L.
通讯作者：
Delp, Scott L.
影响因子：
6.8
作者：
Hansen, N;Muller, SD;Koumoutsakos, P
通讯作者：
Koumoutsakos, P
影响因子：
2.4
作者：
Kaplan, ML;Heegaard, JH
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