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Precise prediction of launch speed for athletes in the aerials event of freestyle skiing based on deep transfer learning
Scientific Reports ( IF 4.6 ) Pub Date : 2023-03-15 , DOI: 10.1038/s41598-023-31355-8
Daqi Jiang 1 , Hong Wang 1 , Jichi Chen 1 , Chuansheng Dong 2
Affiliation  

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.



中文翻译:

基于深度迁移学习的自由式滑雪空中技巧运动员起跳速度精准预测

自动获取发射速度是运动员在自由式滑雪空中技巧项目中取得好成绩的有力保障。在大多数已发表的描述运动员获得高分的研究中,助滑距离完全取决于教练甚至运动员自身的经验,这可能不是最佳的。本论文的主要目标是使用采集系统并开发人工神经网络(ANN)模型来自动获取辅助滑动距离和速度之间的对应关系。在Unity3D引擎中可以模拟雪摩擦系数、风速、风向、坡度、高度和重量的影响。温度、湿度和倾斜角度的影响需要专业测试人员在现实世界中测量,非常费力。神经网络首先通过足够的模拟数据进行训练以获得编码特征。然后,将在模拟环境中学习到的信息传输到另一个网络。第二个网络使用来自 20 名专业测试人员的数据。与没有迁移学习的模型相比,所提方法的性能有显着提高。测试集的均方误差为 0.692。据观察,所设计的深度迁移学习 (DTL) 模型预测的速度与实验测量结果非常吻合。结果表明,所提出的迁移学习方法是一种有效的模型,可用作预测自由式滑雪空中技巧项目中运动员的辅助滑动距离和发射速度的工具。

更新日期:2023-03-15
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