Background: This article details the development of adopting the Nesbit and McGinnis model of the golf swing as a starting point for studying golf performance optimization. The model was selected as it presents an opportunity to examine how non-naïve participants can learn and improve their swing mechanics, which could prove valuable in studying human learning in sports, rehabilitation, and re-education.
Methods: Kinematic data was acquired in laboratory and real-world environments using the motion capture systems Qualysis and CodaMotion CX-Sport, respectively. In the early stages of developing the model in MATLAB, we identified limitations in the Nesbit and McGinnis methodology, including the filtering techniques applied to swing vectors and the selection of swing variables and the solutions to their boundary conditions solutions during the downswing. By addressing these issues, our goal was to revise the model and make it more robust and capable of optimizing the impact velocities from a wider variety of subjects with varying swing mechanics.
Results: By increasing the cutoff frequency used to filter the swing vectors and expanding the swing variable polynomial equations, we found it was possible for all participants to increase their club head velocity at impact while respecting their unique kinematic limitations. The manner of the kinematic changes and the percent of velocity improvement are participant dependent.
Conclusions: Our study showed that the observed and optimized hub paths differed among participants, which suggests participants might also differ in their approaches and capacities to adopt the latter.