Rafique Samina, Najam-Ul-Islam M, Shafique M, Mahmood A
Electrical Engineering Department, Bahria University, Islamabad 44230, Pakistan.
Biomedical Engineering Department, Riphah International University, Islamabad 44230, Pakistan.
Appl Bionics Biomech. 2020 Aug 20;2020:1979342. doi: 10.1155/2020/1979342. eCollection 2020.
Sit-to-stand (STS) motion is an indicator of an individual's physical independence and well-being. Determination of various variables that contribute to the execution and control of STS motion is an active area of research. In this study, we evaluate the clinical hypothesis that besides numerous other factors, the central nervous system (CNS) controls STS motion by tracking a prelearned head position trajectory. Motivated by the evidence for a task-oriented encoding of motion by the CNS, we adopt a robotic approach for the synthesis of STS motion and propose this scheme as a solution to this hypothesis. We propose an analytical biomechanical human CNS modeling framework where the head position trajectory defines the high-level task control variable. The motion control is divided into low-level task generation and motor execution phases. We model CNS as STS controller and its Estimator subsystem plans joint trajectories to perform the low-level task. The motor execution is done through the Cartesian controller subsystem that generates torque commands to the joints. We do extensive motion and force capture experiments on human subjects to validate our analytical modeling scheme. We first scale our biomechanical model to match the anthropometry of the subjects. We do dynamic motion reconstruction through the control of simulated custom human CNS models to follow the captured head position trajectories in real time. We perform kinematic and kinetic analyses and comparison of experimental and simulated motions. For head position trajectories, root mean square (RMS) errors are 0.0118 m in horizontal and 0.0315 m in vertical directions. Errors in angle estimates are 0.55 rad, 0.93 rad, 0.59 rad, and 0.0442 rad for ankle, knee, hip, and head orientation, respectively. RMS error of ground reaction force (GRF) is 50.26 N, and the correlation between ground reaction torque and the support moment is 0.72. Low errors in our results validate (1) the reliability of motion/force capture methods and anthropometric technique for customization of human models and (2) high-level task control framework and human CNS modeling as a solution to the hypothesis. Accurate modeling and detailed understanding of human motion can have significant scope in the fields of rehabilitation, humanoid robotics, and virtual characters' motion planning based on high-level task control schemes.
从坐姿到站姿（STS）的动作是个体身体独立性和健康状况的一个指标。确定有助于执行和控制STS动作的各种变量是一个活跃的研究领域。在本研究中，我们评估了一个临床假设，即除了许多其他因素外，中枢神经系统（CNS）通过跟踪预先学习的头部位置轨迹来控制STS动作。受中枢神经系统对动作进行任务导向编码的证据启发，我们采用机器人方法来合成STS动作，并提出此方案作为该假设的解决方案。我们提出了一个分析性生物力学人体中枢神经系统建模框架，其中头部位置轨迹定义了高级任务控制变量。运动控制分为低级任务生成和运动执行阶段。我们将中枢神经系统建模为STS控制器，其估计器子系统规划关节轨迹以执行低级任务。运动执行通过笛卡尔控制器子系统完成，该子系统向关节生成扭矩命令。我们对人类受试者进行了广泛的运动和力捕捉实验，以验证我们的分析建模方案。我们首先对生物力学模型进行缩放，以匹配受试者的人体测量学特征。我们通过控制模拟的定制人体中枢神经系统模型进行动态运动重建，以实时跟踪捕获的头部位置轨迹。我们进行运动学和动力学分析以及实验和模拟运动的比较。对于头部位置轨迹，水平方向的均方根（RMS）误差为0.0118 m，垂直方向为0.0315 m。踝关节、膝关节、髋关节和头部方向的角度估计误差分别为0.55 rad、0.93 rad、0.59 rad和0.0442 rad。地面反作用力（GRF）的RMS误差为50.26 N，地面反作用扭矩与支撑力矩之间的相关性为0.72。我们结果中的低误差验证了（1）运动/力捕捉方法和用于定制人体模型的人体测量技术的可靠性，以及（2）高级任务控制框架和人体中枢神经系统建模作为该假设的解决方案。对人体运动的准确建模和详细理解在康复、类人机器人以及基于高级任务控制方案的虚拟角色运动规划等领域具有重要意义。
