Sequence Video and Artificial Intelligence Assisted Basketball Injury Risk Early Warning Method

Abstract

Basketball sport is a comprehensive nonperiodic collective sport, in which sports injuries of various parts are also prone to occur. Its higher exercise intensity not only effectively enhances the physical fitness of the athlete but also causes physical damage due to a series of actions such as frequent take-offs and landings, resulting in certain sports injuries. Therefore, the early warning of injury in basketball is very necessary, and it is very useful to protect the safety of players. In this paper, we investigate how to use the sequence video and artificial intelligence method for the basketball injury risk early warning in order to protect the player and better assist the player to improve their efficiency of training. First, we preprocess the video sequence. We convert the video sequence into image data and perform noise reduction and transformation operations on the image. Second, for the processed image data, we designed a convolutional neural network model to determine the damaged area. Third, we use the neural network model to take the image data with the detection area as the input, perform feature extraction on the data, and finally obtain the early warning value of basketball sports injury. The experimental results prove that the method proposed in this paper has good evaluation performance.