Machine learning model to study effects of lightweight racket design on elbow and shoulder loading during tennis serve

Tennis is a famous sport in which players perform high-speed, repetitive movements that cause significant load to the shoulder and elbow joints, particularly during tennis serve. Further, the racket design also plays a key role in the efficiency of the player’s performance through the level of mechanical stress that it places on the player’s elbow joints. Therefore, analyzing the biomechanical impact of racket types on the load it renders on elbow and shoulder loading will help optimize the player’s performance and avoid the possibility of risk. However, there are limited studies that are related to the effect of differences in racket mass, balance, and inertia over joint force and moment during the phases of serve. To address this gap, this study employs a Machine Learning (ML)-based model to impact three types of racket such as head-light, even-balanced, and head-heavy, towards joint moments and forces in the shoulder, elbow, and wrist during tennis serves. The kinematic data was collected from eight tennis players, and the collected data was processed using a Long Short-Term Memory (LSTM) neural network to predict joint moments and forces based on racket parameters and segmental kinematics. The results have shown that racket design has an excellent impact on a player’s performance through its impact on shoulder and elbow joints. The head-light racket resulted in a shoulder adduction moment of 9.4 ± 0.8 Nm and a shoulder joint force of 130.2 ± 9.4 N during the acceleration phase, compared to the head-heavy racket, which generated a higher adduction moment of 11.3 ± 1.0 Nm and a shoulder force of 162.3 ± 11.4 N. The even-balanced racket showed intermediate values, with an adduction moment of 10.2 ± 0.9 Nm and a shoulder force of 142.5 ± 9.9 N.