Beyond animated skeletons: How can biomechanical feedback be used to enhance sports performance? 1 1 Animated skeletons—popularised by a proprietary general-purpose biomechanics modelling and analysis software package—have now largely superseded the humble stickman that has been integral to biomechanical feedback provision for the past few decades. Despite holding great appeal and captivating many an athlete and coach, these visualisations have limited utility in a feedback capacity, especially if presented in isolation, as is often the case.

Abstract

Biomechanical feedback technologies are becoming increasingly prevalent in elite athletic training environments but how the kinematic and kinetic data they produce can be best used to improve sports techniques and enhance sports performance is unclear. This paper draws on theoretical and empirical developments in the motor control, skill acquisition, and sports biomechanics literatures to offer practical guidance and strategic direction on this issue. It is argued that the information produced by biomechanical feedback technologies can only describe, with varying degrees of accuracy, what patterns of coordination and control are being adopted by the athlete but, crucially, it cannot prescribe how these patterns of coordination and control should be modified to enhance sports performance. As conventional statistical and theoretical modelling paradigms in applied sports biomechanics provide limited information about patterns of coordination and control, and do not permit the identification of athlete-specific optimum sports techniques, objective criteria on which to base technical modifications that will consistently lead to enhanced performance outcomes cannot reliably be established for individual athletes. Given these limitations, an alternative approach, which is harmonious with the tenets of dynamical systems theory and aligned with the pioneering insights of Bernstein (1967) on skill acquisition, is advocated. This approach involves using kinematic and kinetic data to channel the athlete’s search towards their own unique ‘optimum’ pattern of coordination and control as they actively explore their perceptual-motor workspace during practice. This approach appears to be the most efficacious use of kinematic and kinetic data given current biomechanical knowledge about sports techniques and the apparent inability of existing biomechanical modelling approaches to accurately predict how technique changes will impact on performance outcomes for individual athletes.