THE PERSONALISED 'DIGITAL ATHLETE': An evolving vision for the capture, modelling and simulation, of on-field athletic performance

Abstract

Technological advances in the areas of three-dimensional (3D) body scanning, in-vivo imaging and novel forms of motion capture and data analytics (e.g. deep learning neural networks) are rapidly bridging the lab versus field-based nexus that has historically plagued the applied sport biomechanist. Similarly, exponential advances in hardware and computer processing power has witnessed the emergence of the personalised 'digital athlete', an overarching vision that facilitates, via the integration of multiple technologies, real-time biomechanical data collection, modelling and reporting for immediate biofeedback.