Degree
Bachelor of Science (B.S.)
Major(s)
Computer Engineering
Document Type
Immediate Open Access
Abstract
A golf swing is biomechanically complex. Professional swing training is expensive for the average golfer. With the growing development of small inertial sensors and powerful microprocessors with built-in wireless communication protocol support, embedded devices are becoming suitable for tough tasks like motion tracking. The proposed solution consists of a sensor-packed golf glove. To evaluate the efficacy of the proposed solution, a recurrent neural network is developed that uses a learning model to identify golf swings that produce a slice, the most common golf swing error. A motion capture system was used as the professional baseline for the evaluation. Barely falling short of the professional solution’s performance, the proposed solution showed potential to become a portable and economical alternative.
DOI
https://doi.org/10.54718/ZOAX2683
Date Defended
5-1-2019
Thesis Director
Ball, John E.
Second Committee Member
Burch V, Reuben F.
Third Committee Member
Elder, Anastasia
Recommended Citation
Fletcher, Jackson D., "Golf Glove Data-based Swing Classification through Machine Learning" (2019). Honors Theses. 50.
https://scholarsjunction.msstate.edu/honorstheses/50
