Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis - Liu, Jiatian (Frontiers in Psychology, 2021)
In order to analyse the sports psychology of athletes and to identify the psychology of athletes in their movements, a human action recognition (HAR) algorithm is designed in this study. First, a HAR model is established based on the convolutional neural network (CNN) to classify the current action state by analysing the action information of task in the collected videos. Secondly, the psychology of basketball players displaying fake actions during the offensive and defensive process is investigated by combining with related sports psychological theories. Then, the psychology of athletes is also analysed through the collected videos, so as to predict the next response action of the athletes. Experimental results show that the combination of grayscale and red-green-blue (RGB) images can reduce the image loss and effectively improve the recognition accuracy of the model. The optimized convolutional 3-dimensional network (C3D) HAR model designed in this study has a recognition accuracy of 80%, with the image loss of 5.6. Besides, the time complexity is reduced by 33%. Therefore, the proposed optimized C3D can recognize effectively the human actions, and the results of this study can provide a reference for the investigation of the image recognition of human action in sports.