Precise prediction of launch speed for athletes in the aerials event of freestyle skiing based on deep transfer learning

Abstract

Automatically obtaining the launch speed are powerful guarantees for athletes in the aerials event of freestyle skiing to achieve good results. In most of the published studies describing athletes getting high scores, the assisting sliding distance depends entirely on the coach and even the athlete’s own experience, which may not be optimal. The main goal of the present paper is to use an acquisition system and develop an artificial neural network (ANN) model to automatically obtain the corresponding relationship between assisting sliding distance and speed. The influence of snow friction coefficient, wind speed, wind direction, slope, height and weight can be simulated in the Unity3D engine. The influence of temperature, humidity and tilt angle needs to be measured in real world by professional testers which is strenuous. The neural network is first trained by sufficient simulation data to obtain the encoded feature. Then, the information learned in simulation environment is transferred to another network. The second network uses the data taken from twenty professional testers. Compared with the model without transfer learning, the performance of proposed method has significant improvement. The mean squared error for the testing set is 0.692. It is observed that the speed predicted by the designed deep transfer learning (DTL) model is in good agreement with the experimental measurement results. The results indicate that the proposed transfer learning method is an efficient model to be used as a tool for predicting the assisting sliding distance and launch speed for athletes in the aerials event of freestyle skiing.