The Application of an Improved Random Forest (RF) Algorithm Integrated Human Posture Recognition Method in the Field of Sports Training

Abstract

Human posture recognition is important in sports training because it allows proper evaluation of the technique of an athlete, enhances performance, and reduces the risk of injury. Conventional techniques, including coach manual evaluation and motion capture systems, are generally timeconsuming, expensive, and not scalable. To solve these issues, the suggested approach combines a Random Forest (RF) model with Deep Learning (DL) methods to develop an adaptive and efficient posture recognition system. The hybrid model applies RF for static posture classification and Bidirectional Long Short-Term Memory (BiLSTM) for Modeling sequential dependencies in synthetic sequences of static postures, created by data augmentation. The uniqueness of this method is that RF and DL are combined to improve posture recognition. Improvement in RF was achieved by applying feature selection techniques (RFE) and hybrid tree pruning, reducing computational overhead while improving classification accuracy. These optimizations make the system more efficient and scalable, suitable for large-scale training environments. The RF model’s accuracy improved by 4.7%, from 92.5% to 97.2%, after the application of feature selection and pruning techniques. The outcomes testify to the proficiency of the model with a 99.08% classification accuracy, 99.28% average precision, 99.18% recall, and F1-score of 99.38%. The model also evidenced good performance over a range of sports postures, with maximum accuracy of 99.66% in sections like Axe Throwing. This method is of great benefit compared to conventional techniques by providing a computationally cost-effective, scalable, and highly accurate posture recognition system, which ultimately leads to improved sports training results and the minimization of injury risks.