Woodball swing events detection using least square method.

Amateur woodball players often encounter challenges when trying to practice their playing stances and swing mechanisms without proper coaching advice, particularly those who cannot afford high coaching fees. The absence of supervision from coaches may lead these players to unconsciously repeat incorrect postures during their swing, hindering their progress. To address this issue, this study aims to develop an automated procedure utilizing the Least Square (LS) method to measure woodball players' swing techniques and provide scientific feedback to enhance their skills. For this research, a group of thirty woodball players with diverse characteristics, including different genders, ages, weights, and heights, performed a full woodball swing motion five times each. To measure the acceleration, angle, and angular velocity of the swing, an Inertial Measurement Unit (IMU) sensor was attached to the players' left-hand wrist. The signals collected were then underwent processing with a second-order Butterworth low-pass filter, estimated using the LS method, and classified based on the reference swing events. The study yielded promising results, as it achieved an accurate event detection rate with an average accuracy of over 99%. [ABSTRACT FROM AUTHOR]