Accuracy of pattern detection methods in the performance of golf putting

Area of Science:

• Sports Science
• Biomechanics
• Motor Control

Background:

• Golf putting performance relies on complex motor coordination.
• Objective analysis of putting technique is crucial for skill improvement.

Purpose of the Study:

• To compare the classification accuracy of five pattern detection methods for golf putting.
• To identify a unique 'putting signature' for individual golfers.
• To assess the applicability of these methods in motor control studies.

Main Methods:

• Computer vision for golf club position detection.
• Darwinian particle swarm optimization for kinematic model estimation.
• Classification using linear discriminant analysis, quadratic discriminant analysis, naive Bayes (normal distribution and kernel smoothing), and least squares support vector machines.

Main Results:

• The study evaluated the classification accuracy of five distinct pattern detection algorithms.
• Individualized 'putting signatures' were successfully identified for each golf player.
• The methods demonstrated potential for analyzing coordination and motor control in putting.

Conclusions:

• The presented pattern detection methods are effective for classifying golf putting performance.
• These techniques offer valuable insights into intra- and interpersonal variability in motor behavior.
• The findings support the application of these methods in sports science and motor control research.