Based on the clinical evidence that head position measured by the multisensory system contributes to motion control, this study suggests a biomechanical human-central nervous system modeling and control framework for sit-to-stand motion synthesis. Motivated by the evidence for a task-oriented encoding of motion by the central nervous system, we propose a framework to synthesize and control sit-to-stand motion using only head position trajectory in the high-level-task-control environment. First, we design a generalized analytical framework comprising a human biomechanical model and an adaptive neuro-fuzzy inference system to emulate central nervous system. We introduce task-space training algorithm for adaptive neuro-fuzzy inference system training. The adaptive neuro-fuzzy inference system controller is optimized in the number of membership functions and training cycles to avoid over-fitting. Next, we develop custom human models based on anthropometric data of real subjects. Using the weighting coefficient method, we estimate body segment parameter. The subject-specific body segment parameter values are used (1) to scale human model for real subjects and (2) in task-space training to train custom adaptive neuro-fuzzy inference system controllers. To validate our modeling and control scheme, we perform extensive motion capture experiments of sit-to-stand transfer by real subjects. We compare the synthesized and experimental motions using kinematic analyses. Our analytical modeling-control scheme proves to be scalable to real subjects’ body segment parameter and the task-space training algorithm provides a means to customize adaptive neuro-fuzzy inference system efficiently. The customized adaptive neuro-fuzzy inference system gives 68%–98% improvement over general adaptive neuro-fuzzy inference system. This study has a broader scope in the fields of rehabilitation, humanoid robotics, and virtual characters’ motion planning based on high-level-task-control scheme.

中文翻译：

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使用头部位置跟踪对坐到站运动的神经模糊控制

基于多感觉系统测量的头部位置有助于运动控制的临床证据，本研究提出了一种用于坐到站运动合成的生物力学人体 - 中枢神经系统建模和控制框架。受中枢神经系统以任务为导向的运动编码证据的启发，我们提出了一个框架，在高级任务控制环境中仅使用头部位置轨迹来合成和控制从坐到站的运动。首先，我们设计了一个广义的分析框架，包括一个人体生物力学模型和一个自适应神经模糊推理系统来模拟中枢神经系统。我们引入了用于自适应神经模糊推理系统训练的任务空间训练算法。自适应神经模糊推理系统控制器在隶属函数和训练周期的数量上进行了优化，以避免过拟合。接下来，我们根据真实受试者的人体测量数据开发自定义人体模型。使用加权系数法，我们估计体节参数。特定于受试者的身体段参数值用于 (1) 为真实受试者缩放人体模型和 (2) 在任务空间训练中训练自定义自适应神经模糊推理系统控制器。为了验证我们的建模和控制方案，我们对真实受试者进行了大量的从坐到站转移的动作捕捉实验。我们使用运动学分析比较合成运动和实验运动。我们的分析建模控制方案证明可扩展到真实受试者的身体部位参数，并且任务空间训练算法提供了一种有效定制自适应神经模糊推理系统的方法。定制的自适应神经模糊推理系统比一般自适应神经模糊推理系统提高了 68%–98%。本研究在康复、仿人机器人和基于高级任务控制方案的虚拟角色运动规划领域具有更广泛的范围。