Examining the Effectiveness of K-Means Clustering Using Minkowski Distances on Spatial Data of Tennis Serve Pose for sports players to maintain good health

Abstract

Introduction: In this paper, the simulation & qualification of the improvement of tennis stance for player performance improvement using 2d analysis of videos taken from a mobile camera is presented along with the simulation results. This research introduces an innovative approach to improving tennis performance by optimizing players' biomechanical stances during specific shots, using advanced 2D video analysis combined with Recurrent Neural Networks (RNNs). By employing precise pose estimation algorithms, the study meticulously captures skeletal keypoints to calculate joint angles using vector dot product calculations. These keypoints provide a detailed biomechanical analysis and allow for the categorization of movement patterns through unsupervised clustering techniques like k-means. The study further enhances the accuracy of these analyses by employing adaptive acceptance areas defined by various distance metrics, addressing challenges such as motion artifacts, fluctuating lighting conditions, and low signal-to-noise ratios with high-SNR imaging equipment and finely tuned camera calibration. The methodology ensures the capture of high-quality data crucial for effective computational analysis. It utilizes cloud computing to process data while ensuring data confidentiality and leveraging the scalability of computational resources. This robust integration supports detailed kinematic analysis via part affinity fields and TensorFlow Lite, facilitating immediate feedback on players’ movements and biomechanical alignment. This research significantly advances the field by integrating sophisticated computational algorithms and customized hardware solutions that go beyond the constraints of conventional video analysis. The effectiveness of this model, demonstrated through the research, has potential applications across various scientific and engineering fields. The simulations shows the effectiveness of the methodology that is being developed by us.
Objectives: The objective of this paper is to present a novel method for improving tennis player performance through the optimization of biomechanical stances, using advanced 2D video analysis and Recurrent Neural Networks (RNNs). This study utilizes precise pose estimation algorithms to capture skeletal keypoints for joint angle calculations and movement pattern categorization, enhancing the accuracy with adaptive acceptance areas to counteract common data acquisition challenges. Ultimately, the research aims to establish a new standard in sports performance analysis by providing detailed, real-time biomechanical feedback, leveraging cloud computing for data processing, and advancing the integration of technology in tennis coaching.
Methods: The methods used in this paper is to enhance tennis player performance by optimizing biomechanical stances through a sophisticated method of 2D video analysis and Recurrent Neural Networks (RNNs). By employing precise pose estimation algorithms, this study meticulously captures and analyzes skeletal keypoints to determine joint angles and categorize movement patterns, overcoming challenges such as motion artifacts and fluctuating lighting conditions.
Results:The results of this study demonstrated significant improvements in player performance, with the enhanced biomechanical stances leading to more efficient and effective shot execution. The analysis confirmed the effectiveness of the methodology, showing a marked reduction in movement inefficiencies and increased consistency in players' shots across various lighting conditions.
Conclusions: The study concludes that the integration of 2D video analysis and Recurrent Neural Networks significantly enhances the biomechanical analysis of tennis players, improving overall performance through optimized stances. These findings suggest potential broader applications of this technology in sports training and coaching, promising substantial advancements in the field of sports performance analysis.