James Worth Bagley College of Engineering
Department of Electrical and Computer Engineering
Bachelor of Science (B.S.)
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.
Ball, John E.
Burch V, Reuben F.
Fletcher, Jackson D., "Golf Glove Data-based Swing Classification through Machine Learning" (2019). Honors Theses. 50.