Wilshin Simon, Haynes G Clark, Porteous Jack, Koditschek Daniel, Revzen Shai, Spence Andrew J
Structure and Motion Lab, Royal Veterinary College, Hawkshead Lane, Herts, AL9 7TA, UK.
Uber Advanced Technologies Center, 3011 Smallman St, Pittsburgh, PA, 15201, USA.
Biol Cybern. 2017 Aug;111(3-4):269-277. doi: 10.1007/s00422-017-0721-2. Epub 2017 Jun 19.
Gaits and gait transitions play a central role in the movement of animals. Symmetry is thought to govern the structure of the nervous system, and constrain the limb motions of quadrupeds. We quantify the symmetry of dog gaits with respect to combinations of bilateral, fore-aft, and spatio-temporal symmetry groups. We tested the ability of symmetries to model motion capture data of dogs walking, trotting and transitioning between those gaits. Fully symmetric models performed comparably to asymmetric with only a [Formula: see text] increase in the residual sum of squares and only one-quarter of the parameters. This required adding a spatio-temporal shift representing a lag between fore and hind limbs. Without this shift, the symmetric model residual sum of squares was [Formula: see text] larger. This shift is related to (linear regression, [Formula: see text], [Formula: see text]) dog morphology. That this symmetry is respected throughout the gaits and transitions indicates that it generalizes outside a single gait. We propose that relative phasing of limb motions can be described by an interaction potential with a symmetric structure. This approach can be extended to the study of interaction of neurodynamic and kinematic variables, providing a system-level model that couples neuronal central pattern generator networks and mechanical models.
步态及步态转换在动物运动中起着核心作用。对称性被认为支配着神经系统的结构,并限制四足动物的肢体运动。我们针对双侧、前后以及时空对称群的组合来量化狗的步态对称性。我们测试了对称性对狗行走、小跑以及在这些步态之间转换时的运动捕捉数据进行建模的能力。完全对称模型的表现与非对称模型相当,残差平方和仅增加了[公式:见原文],且参数数量仅为其四分之一。这需要添加一个代表前后肢之间延迟的时空偏移量。若没有这个偏移量,对称模型的残差平方和会大[公式:见原文]。这种偏移与(线性回归,[公式:见原文],[公式:见原文])狗的形态有关。这种对称性在整个步态及转换过程中都得到遵循,这表明它在单个步态之外也具有普遍性。我们提出肢体运动的相对相位可以通过具有对称结构的相互作用势来描述。这种方法可以扩展到对神经动力学和运动学变量相互作用的研究,提供一个将神经元中枢模式发生器网络和力学模型耦合起来的系统级模型。