Application and Implementation of Deep Learning for Evaluation of Martial Arts Trainings

Abstract

The Chinese name for martial arts is known as Wushu; it is a typical Chinese sport in which both internal and exterior actions are emphasized. Wushu training evaluation is a hot research area, and deep learning has long been an essential tool for ensuring and promoting continuous development in the quality of Wushu training assessment. The deep learning-based martial arts training programme successfully processes and analyses the massive raw data produced throughout the teaching process at colleges and institutions. The online learning behavior is obtained by training the detection model of target, model for detection of face, and face segmentation model and then merging them with the online system. Feature extraction, offline performance prediction, learning law analysis, and personalized learning recommendation can provide decision support for training of martial arts evaluation as well as the formulation of related improvement measures. It can successfully increase the teaching quality of teachers and the learning efficiency of students by catering to the current online and offline combination of new learning and teaching techniques. In this paper, a martial arts training evaluation model based on deep learning technology is presented in light of the variety and vast quantity of martial arts training evaluations, using MatConvNet to build a deep neural network and organically fuse various raw data of martial arts training evaluation. The proposed approach provides a more accurate assessment of martial arts training and has some practical applications. In experimental evaluation of the model, it is obtained that the network's prediction performance is at its greatest value when the combination number of layers are eight.