Department of Mechanical Engineering, University of Alberta, Donadeo Innovation Centre for Engineering, Edmonton, Alberta T6G 1H9, Canada.
Glenrose Rehabilitation Hospital, Edmonton, Alberta, Canada.
J Neural Eng. 2023 Apr 12;20(2). doi: 10.1088/1741-2552/acc54f.
Characterizing the task goals of the neural control system for achieving seated stability has been a fundamental challenge in human motor control research. This study aimed to experimentally identify the task goals of the neural control system for seated stability.Ten able-bodied young individuals participated in our experiments, which allowed us to measure their body motion and muscle activity during perturbed sitting. We used a nonlinear neuromechanical model of the seated human, along with a full-state feedback linearization approach and optimal control theory for identifying the neural control system and characterizing its task goals.We demonstrated that the neural feedback for trunk stability during seated posture uses angular position, velocity, acceleration, and jerk in a linearized space. The mean squared error between the predicted and measured motor commands was less than 0.6% among all trials and participants, with a median correlation coefficientrof more than 0.9. Our identified optimal neural control primarily used trunk angular acceleration and near-minimum muscle activation to achieve seated stability while keeping the deviations of the trunk angular position and acceleration sufficiently small.Our proposed approach to neural control system identification relied on a performance criterion (e.g. cost function) explaining what the functional goal is and subsequently, finds the control law that leads to the best performance. Therefore, instead of assuming what control schemes the neural control might utilize (e.g. proportional-integral-derivative control), optimal control allows the motor task and the neuromechanical model to dictate a control law that best describes the physiological process. This approach allows for a mechanistic understanding of the neuromuscular mechanisms involved in seated stability and for inferring the task goals used by the neural control system to achieve the targeted motor behavior. Such neural control characterization can contribute to the development of objective balance evaluation tools and of bio-inspired assistive neuromodulation technologies.
描述实现坐姿稳定的神经控制系统的任务目标一直是人类运动控制研究中的一个基本挑战。本研究旨在通过实验确定坐姿稳定性的神经控制系统的任务目标。十名健康的年轻人参加了我们的实验，使我们能够在受扰坐姿时测量他们的身体运动和肌肉活动。我们使用了坐姿人体的非线性神经力学模型，以及全状态反馈线性化方法和最优控制理论来识别神经控制系统并描述其任务目标。我们证明了在坐姿中，用于躯干稳定性的神经反馈使用线性化空间中的角位置、速度、加速度和冲击。所有试验和参与者的预测和测量运动指令之间的均方误差小于 0.6%，中位数相关系数大于 0.9。我们确定的最优神经控制主要使用躯干角加速度和接近最小肌肉激活来实现坐姿稳定性，同时保持躯干角位置和加速度的偏差足够小。我们提出的神经控制系统识别方法依赖于一个性能标准（例如成本函数）来解释功能目标是什么，然后找到导致最佳性能的控制律。因此，与假设神经控制可能使用的控制方案（例如比例积分微分控制）不同，最优控制允许运动任务和神经力学模型来规定最能描述生理过程的控制律。这种方法可以深入了解涉及坐姿稳定性的神经肌肉机制，并推断出神经控制系统用于实现目标运动行为的任务目标。这种神经控制特征可以为开发客观的平衡评估工具和仿生辅助神经调节技术做出贡献。
