Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea.
Department of Mechanical and Industrial Engineering, Indian Institute of Technology Roorkee, Uttarakhand 247667, India.
Sensors (Basel). 2018 Nov 7;18(11):3819. doi: 10.3390/s18113819.
The automatic classification of cross-country (XC) skiing techniques using data from wearable sensors has the potential to provide insights for optimizing the performance of professional skiers. In this paper, we propose a unified deep learning model for classifying eight techniques used in classical and skating styles XC-skiing and optimize this model for the number of gyroscope sensors by analyzing the results for five different configurations of sensors. We collected data of four professional skiers on outdoor flat and natural courses. The model is first trained over the flat course data of two skiers and tested over the flat and natural course data of a third skier in a leave-one-out fashion, resulting in a mean accuracy of ~80% over three combinations. Secondly, the model is trained over the flat course data of three skiers and tested over flat course and natural course data of one new skier, resulting in a mean accuracy of 87.2% and 95.1% respectively, using the optimal sensor configuration (five gyroscope sensors: both hands, both feet, and the pelvis). High classification accuracy obtained using both approaches indicates that this deep learning model has the potential to be deployed for real-time classification of skiing techniques by professional skiers and coaches.
利用可穿戴传感器数据对越野滑雪技术进行自动分类，有可能为优化专业滑雪运动员的表现提供深入的见解。在本文中，我们提出了一种用于对传统和滑冰式越野滑雪的八种技术进行分类的统一深度学习模型，并通过分析五种不同传感器配置的结果来针对陀螺仪传感器的数量对该模型进行优化。我们在户外平地和自然赛道上收集了四名专业滑雪者的数据。该模型首先在两名滑雪者的平地赛道数据上进行训练，并以三选一的方式在第三名滑雪者的平地和自然赛道数据上进行测试，三种组合的平均准确率约为 80%。其次，该模型在三名滑雪者的平地赛道数据上进行训练，并在一名新滑雪者的平地和自然赛道数据上进行测试，使用最佳传感器配置（五个陀螺仪传感器：双手、双脚和骨盆），分别得到 87.2%和 95.1%的平均准确率。这两种方法都获得了较高的分类准确率，表明该深度学习模型有可能被专业滑雪运动员和教练用于实时滑雪技术分类。
