Study on the impact of shoulder flexibility training on smash speed mechanics in badminton using machine learning

The smash stroke in badminton is a key attack style that makes the opponent player miss the strike; the smash requires speed, agility, strength, and precision. The smash demands a high level of shoulder flexibility from the players, which increases the Range of Motion (ROM) during the backswing and forward swing phases. The shoulder flexibility provides excellent energy storage and transfer, improving smash speed. The biomechanical efficiency of Shoulder Flexibility Training (FLT) on smash speed efficiency is still under study. This lack of study leads to modelling training program limitations, which may increase the risk of injury. Examining the process by which smash speed mechanics are impacted by Shoulder Flexibility Training (SFT) programs is the primary goal of the present investigation. It is approximately a 6-week training program for Amateur Players (AP) and National Players (NP), which uses core shoulder motions like flexion, abduction, and rotation as its basis. Motion capture systems and radar sensors investigated joint motion and smash speeds. To address the shortage of study evidence on the subject, a hybrid CNN + LSTM model was applied to predict smash speed concerning improved shoulder flexibility. As reported by the research results, students’ smash speed and shoulder flexibility improved significantly during training. There was a 4.35% boost to smash speed at contact and a 4.69% gain in shoulder internal rotation compared to non-contact athletes. Additionally, there was a 9.83% boost in smash speed at contact and a 9.76% boost in shoulder internal rotation for the best athletes. Considering post-training illnesses, the CNN + LSTM model successfully predicted smash speed, with R³ scores of 0.99 for NP and 0.97 for AP.