We begin by considering 3D digital tools such as XROMM and musculoskeletal modelling; such tools may be used in empirical contexts to address questions about locomotor or appendicular dynamics. We then explore the spectrum (explained below) of ways that researchers in biomechanics can use digital methods to understand locomotor mechanisms, such as finite element analysis (FEA; see Glossary) and dynamic simulation. This spectrum proceeds from more empirical toward more theoretical analyses, but all analyses here considered arguably involve a form of ‘model’ (see Glossary) that their digital methods visually represent. Next, we investigate how integration of digital tools across this spectrum achieves novel, exciting understanding of motion. Finally, we explore the pitfalls and challenges involved in these approaches; and current frontiers at the cutting edge of using 3D digital tools and methods in tetrapod locomotor biomechanics. Our review demonstrates how tools have matured to the point where now we can use them in isolation or integration to answer fundamental questions we never could have tackled two decades ago.

Conceptual model

An abstraction of an organism to a small number of parameters to investigate fundamental functional principles.

Data overfitting

When a model is adjusted to match its training or validation set too closely, and is thus unable to generalise to new datasets.

Degree(s) of freedom (DOF)

The number of parameters that can vary in a system (e.g. axes of joint motion).

Finite element analysis (FEA)

Estimating stress or strain using smaller components subjected to load(s).

Forward dynamic simulation

Simulation that solves a differential equation of a system's physics over incremental timesteps.

Forward kinematics

Using joint angles to estimate end (e.g. foot) positions.

Inverse dynamic/static simulation

Simulation that solves joint moments (and potentially muscle forces and activations) from input kinematics and kinetics. In the static case, static equilibrium is assumed.

Inverse kinematics

Using an endpoint of a series of segments (e.g. foot) to estimate joint angles.

Multi-body dynamics analysis (MDA)

Rigid body mechanics.

Model

A representation of reality, used to understand reality.

Model evaluation

‘Validation’, i.e. testing how well theoretical predictions match empirical data.

Moment arm

Leverage of a force around a rotational centre.

Musculoskeletal model

A skeletal framework around which the geometry of muscle–tendon units is positioned.

Optimal control

A set of methods to find inputs to a time-dependent system that minimises an objective function.

Precision

The reproducibility, or repeated variation, of a given measurement.

Predictive simulation

Estimating system outputs using only inputs of optimisation criteria and constraints.

Robustness

How changes in model inputs influence output fidelity to empirical data.

Sensitivity analysis

Varying model/simulation input parameters or assumptions to quantify variation of the output data.

Synergistic approach

Combination of empirical and dynamic simulation data, enhanced by the benefits of both.

Tracking simulation

Conducting a simulation with an objective to best match input empirical data.

Verification

Testing the mathematical validity of the design of a model or simulation.

XROMM

X-ray reconstruction of moving morphology: animation of a 3D skeletal marionette using biplanar X-ray video data.