Abstract
Soccer is recognized as one of the most widely played and commercially significant sports globally. An athlete’s performance is typically impaired during states of fatigue. Investigating the body mechanics of soccer players under states of fatigue can provide insights for coaches regarding the physical capabilities and movement deficiencies of their athletes. This understanding can facilitate the adjustment of game strategies and the development of tailored training regimens following competitive matches. Advancements in artificial intelligence have led to the maturation of image recognition technologies, which are increasingly applied across various industries, including promising applications within the realm of soccer. Consequently, this study analyzed a cohort of 5 amateur soccer players (mean age 19.8 years; mean height 1.82 m; mean weight 73.6 kg). The study involved participants who completed 10 shootings on goal as a control group before the implementation of a fatigue protocol, followed by an additional 10 shootings on goal as an experimental group after the completion of the fatigue protocol. Mediapipe image recognition tools and high-speed cameras were utilized to capture data on the various skeletal nodes of the athletes’ bodies and the velocity of the shooting ball, which were subsequently analyzed. The results of the study revealed that when in a state of fatigue, there were significant alterations in the angular displacement of the hip and knee joints in comparison to the ankle joint during shooting by soccer players. The decreased angular displacement of the hip and knee joints resulted in inferior contact between the foot and the ball, as well as a reduction in the speed applied to the ball, leading to a decline in shooting accuracy and ball speed. The findings substantiate the impact of fatigue on the shooting movements of athletes.
Published Version
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