Kinematical and emg analysis of selected offensive strokes using different racket coverings in table tennis

Abstract

Physical education and sports enhance health, well-being, and social integration. Research in newlinethis area focuses on sports biomechanics to understand human physiology and performance. newlineThis thesis explores table tennis, known for agility and precision, examining its biomechanics newlineto improve injury prevention and performance. Originating in the 19th century, table tennis newlineevolved from a parlor game to an Olympic sport, highlighting its competitive nature. The game newlinerequires physical and mental skills, with racket coverings influencing play style. Offensive newlinestrokes, biomechanics, muscle activation, and kinematics are crucial for mastering table tennis, newlineproviding insights into technique, efficiency, and performance optimization. This study newlineexplores electromyography (EMG) in table tennis, examining muscle activation and newlinecoordination during strokes. It also investigates the impact of racket coverings (inverted, short newlinepips, and long pips) on offensive strokes. The study aims to compare EMG data of selected newlineupper body muscles and kinematic variables (racket and ball velocity) using inverted and shortpip newlinecoverings during forehand drive, flick, and smash strokes. Hypotheses assert no significant newlineinfluence of racket coverings on muscle activation or kinematic variables. The study is limited newlineto 30 female table tennis players (mean ± SD; age 20.12 ± 2.38 years; height 163 ± 7.5 cm; newlineweight 56.5 ± 5.2 kg), who participated in national or international tournaments. The study newlinefocused on specific upper body muscles and kinematic variables, such as ball and racket newlinevelocity, in three offensive table tennis strokes using offensive racket coverings. Challenges newlineduring data collection included environmental factors like humidity affecting electrode newlineadhesion and technical issues with EMG devices, potentially impacting data reliability.