Emergence of the advancing neuromechanical phase in a resistive force dominated medium

TL;DR

This study uses a simple model to predict neuromechanical phase lags in sandfish locomotion, revealing fundamental mechanisms of undulatory movement in granular media and aiding robotic design.

Contribution

It demonstrates that a simple, parameter-free model can accurately predict neuromechanical phase lags in sandfish sand-swimming, clarifying their origin.

Findings

The model accurately predicts NPL during sand-swimming.

Synchronized and local torques are key to NPL.

Sand-swimming serves as a model for neuromechanical studies.

Abstract

Undulatory locomotion, a gait in which thrust is produced in the opposite direction of a traveling wave of body bending, is a common mode of propulsion used by animals in fluids, on land, and even within sand. As such it has been an excellent system for discovery of neuromechanical principles of movement. In nearly all animals studied, the wave of muscle activation progresses faster than the wave of body bending, leading to an advancing phase of activation relative to the curvature towards the tail. This is referred to as "neuromechanical phase lags" (NPL). Several multi-parameter neuromechanical models have reproduced this phenomenon, but due to model complexity the origin of the NPL has proved difficult to identify. Here we use perhaps the simplest model of undulatory swimming to accurately predict the NPL during sand-swimming by the sandfish lizard, with no fitting parameters. The sinusoidal wave used in sandfish locomotion, the friction-dominated and non-inertial granular resistive force environment, and the simplicity of the model allow detailed analysis, and reveal the fundamental mechanism responsible for the phenomenon: the combination of synchronized torques from distant points on the body and local traveling torques. This general mechanism should help explain the NPL in organisms in other environments; we therefore propose that sand-swimming could be an excellent system to quantitatively generate and test other neuromechanical models of movement. Such a system can also provide guidance for the design and control of robotic undulatory locomotors in complex environments.