Location Matters-Can a Smart Golf Club Detect Where the Club Face Hits the Ball?

Area of Science:

• Sports Science
• Biomechanics
• Machine Learning

Background:

• Accurate golf ball impact location is crucial for ball flight.
• Current measurement methods are stationary and external.
• A mobile solution is needed for real-time feedback during practice.

Purpose of the Study:

• To investigate the feasibility of detecting golf club impact location using motion sensors.
• To develop a mobile system for real-time impact feedback.
• To assess the accuracy of a machine learning model for impact location classification.

Main Methods:

• An experiment was conducted with one golfer performing 282 golf swings using a 7 iron.
• Impact location was recorded using Trackman technology.
• Golf club motion data was captured using an Inertial Measurement Unit (IMU).
• A ResNet neural network was designed and trained to classify impact location based on motion data.

Main Results:

• A classification accuracy of 93.8% was achieved in estimating the impact location.
• The ResNet architecture effectively utilized motion data for classification.
• The study demonstrated the potential of mobile sensors for golf analysis.

Conclusions:

• Motion sensors on golf club shafts can accurately detect impact location.
• This technology offers a viable, mobile alternative to stationary measurement systems.
• The findings support the development of advanced training tools for golfers.