Abstract
One of the most popular sports in Asia is badminton. Asia has the most talented players in badminton. The players in the conventional badminton teaching are totally reliant on the coach and the fitness instructor. But in today’s technologically advanced world, the educational system has caught up with technology in intelligence. By using the smart technology, the gamers may train on their own. These technologies range from mobile scheduling apps to fitness apps. In this study, a unique intelligent K-Nearest Neighbor algorithm is used, and the outcomes are assessed. Additionally, all conversation is recorded and made available in the database using the Deep K-Nearest Neighbor (DKNN) method. This DKNN model will perform the analysis of neighbor points about the badminton sports activities for training the players with the utilization of Internet of Things (IoT). Results indicate that the suggested DKNN algorithm performed 89% accurately, which is 6.5 percent better than the current sport motion segmentation technique. People may be able to accomplish enough while introducing this method as a real-time application to prepare for subsequent sports and its computing techniques.
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