Prediction of Human Gait Adaptations Using Optimization principles

使用优化原理预测人类步态适应

• 批准号: 0302259
• 负责人: Antonie van den Bogert
• 金额: $ 32.72万
• 依托单位: Cleveland Clinic Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-11-01 至 2004-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0302259&HistoricalAwards=false
• 关键词: Prediction; Human; Gait; Adaptations; Using

0302259van den BogertHumans typically perform movements in a repeatable manner, and adjust their movement to requirements of the task and to constraints imposed by the musculoskeletal system. For instance, a prosthetic limb will lead to an abnormal gait that is not a direct consequence of the device, but is rather the result of adaptations in the control of muscles in the unaffected part of the body. This research tests the hypothesis that such adaptations are governed by a general optimization principle, and can therefore be predicted. Musculoskeletal modeling and optimization will be used to predict the adaptation of human gait to ten different conditions: five speeds, two uphill slopes, two downhill slopes, and walking backwards. Theoretical predictions will be made using four optimization principles: minimal fatigue, minimal metabolic energy, minimal muscle force, minimal perceived effort. The predictions will be compared to the corresponding responses in twelve human subjects to test their validity, with expectations that the principle of minimal metabolic energy will produce the best predictions. A broader impact of this research will include its contribution to a unification of motor control, biomechanics, and bioenergetics for human locomotion. The findings will be applicable to the design of rehabilitation devices, prosthetics, surgical interventions, human-operated machines, and sport equipment. The computational techniques will be made publicly available through an interactive web interface for a target audience of high school and undergraduate students. Visitors will be able to define task conditions and optimization criterion and use the modeling engine to predict human behavior, which will be visualized using animation.

0302259van den Bogert人类通常以可重复的方式执行运动，并根据任务的要求和肌肉骨骼系统施加的约束来调整他们的运动。例如，假肢会导致步态异常，这不是该设备的直接后果，而是身体未受影响部分肌肉控制适应的结果。这项研究测试的假设，这种适应是由一个一般的优化原则，因此可以预测。肌肉骨骼建模和优化将用于预测人类步态对十种不同条件的适应：五种速度，两种上坡，两种下坡和向后行走。理论预测将使用四个优化原则：最小的疲劳，最小的代谢能量，最小的肌肉力量，最小的感知努力。这些预测将与12名人类受试者的相应反应进行比较，以测试其有效性，并期望最小代谢能量原则将产生最佳预测。这项研究的更广泛的影响将包括其对人类运动的运动控制，生物力学和生物能量学的统一的贡献。研究结果将适用于康复设备，假肢，外科手术，人工操作的机器和运动器材的设计。计算技术将通过交互式网络界面向高中和本科生目标受众公开提供。访问者将能够定义任务条件和优化标准，并使用建模引擎来预测人类行为，这些行为将使用动画进行可视化。