Biomechanical analysis and tactical awareness cultivation of badminton players’ variable speed running training

In recent years, the combination of machine learning (ML) and computer vision has influenced sports training approaches, notably for monitoring player performance. This research gives a detailed biomechanical analysis of badminton players during speed-running training, using insights from ML techniques. Key biomechanical metrics such as gait, speed, and acceleration are assessed by tracking players’ motions and the dynamics of their running patterns using computer vision techniques. The badminton stroke video dataset was collected from the Kaggle source. To ensure high-quality input for analysis, the data preprocessing stages include video stabilization with the Kalman filter, noise reduction with Gaussian smoothing, and frame extraction using temporal sampling. Feature extraction approaches like the histogram of oriented gradients (HOG) are used for shape recognition and optical flow for motion tracking. The study provides the use of a simulation environment built on a Modified Ant Lion Optimized Decision Trees (MALO+DT) model trained on historical training data, which allows for the prediction of player movement and biomechanical adjustments based on contextual features such as environment variations and player fatigue. The findings demonstrate that speed running training improves tactical awareness and decision-making in dynamic environments. The performance of the suggested approach was evaluated on the Python platform. The model achieves good prediction accuracy (98.3%), recall (97.4%), F1-score (98%), and precision (97.5%), demonstrating a model's abilities for analysis the effect of training on player biomechanics. Furthermore, the significance of this study is assessed for tactical awareness development, providing coaches and analysts with actionable insights to enhance practices and increase player performance. The findings show that combining biomechanical analysis with speed running training significantly improves players’ adaptability and responsiveness during matches, resulting in a more strategic approach to badminton teaching.