Institute for Neural Computation, Ruhr University Bochum, Bochum, Germany.
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Sci Rep. 2022 May 17;12(1):8189. doi: 10.1038/s41598-022-11102-1.
Existing models of human walking use low-level reflexes or neural oscillators to generate movement. While appropriate to generate the stable, rhythmic movement patterns of steady-state walking, these models lack the ability to change their movement patterns or spontaneously generate new movements in the specific, goal-directed way characteristic of voluntary movements. Here we present a neuromuscular model of human locomotion that bridges this gap and combines the ability to execute goal directed movements with the generation of stable, rhythmic movement patterns that are required for robust locomotion. The model represents goals for voluntary movements of the swing leg on the task level of swing leg joint kinematics. Smooth movements plans towards the goal configuration are generated on the task level and transformed into descending motor commands that execute the planned movements, using internal models. The movement goals and plans are updated in real time based on sensory feedback and task constraints. On the spinal level, the descending commands during the swing phase are integrated with a generic stretch reflex for each muscle. Stance leg control solely relies on dedicated spinal reflex pathways. Spinal reflexes stimulate Hill-type muscles that actuate a biomechanical model with eight internal joints and six free-body degrees of freedom. The model is able to generate voluntary, goal-directed reaching movements with the swing leg and combine multiple movements in a rhythmic sequence. During walking, the swing leg is moved in a goal-directed manner to a target that is updated in real-time based on sensory feedback to maintain upright balance, while the stance leg is stabilized by low-level reflexes and a behavioral organization switching between swing and stance control for each leg. With this combination of reflex-based stance leg and voluntary, goal-directed control of the swing leg, the model controller generates rhythmic, stable walking patterns in which the swing leg movement can be flexibly updated in real-time to step over or around obstacles.
现有的人类行走模型使用低级反射或神经振荡器来产生运动。虽然这些模型适合生成稳定、有节奏的行走模式,但它们缺乏改变运动模式或自发产生新运动的能力,而这些新运动是自愿运动的特征。在这里,我们提出了一个人类运动的神经肌肉模型,它弥补了这一差距,并将执行目标导向运动的能力与产生稳定、有节奏的运动模式结合起来,这些运动模式是稳健运动所必需的。该模型代表了摆动腿在摆动腿关节运动学任务水平上的自愿运动目标。在任务水平上生成平滑的运动计划,以达到目标配置,并使用内部模型将计划的运动转换为下降的运动指令。运动目标和计划根据感觉反馈和任务约束实时更新。在脊髓水平,摆动阶段的下降命令与每个肌肉的通用伸展反射整合。支撑腿控制仅依赖于特定的脊髓反射途径。脊髓反射刺激Hill 型肌肉,激活具有八个内部关节和六个自由体自由度的生物力学模型。该模型能够生成摆动腿的自愿、目标导向的伸展运动,并以有节奏的序列组合多个运动。在行走过程中,摆动腿以目标导向的方式移动到一个实时更新的目标,以根据感觉反馈保持直立平衡,而支撑腿则通过低级反射和行为组织稳定,在每个腿的摆动和支撑控制之间切换。通过这种基于反射的支撑腿和摆动腿自愿、目标导向控制的结合,模型控制器生成了有节奏、稳定的行走模式,其中摆动腿的运动可以灵活地实时更新,以跨过或绕过障碍物。