ECsens, Department of Electronics and Computer Technology, Sport and Health University Research Institute (iMUDS-UGR), Research Centre for Information and Communications Technologies (CITIC-UGR), University of Granada, 18071 Granada, Spain.
Department of Computer Engineering, Automatics and Robotics, Research Centre for Information and Communications Technologies (CITIC-UGR), University of Granada, 18071 Granada, Spain.
Sensors (Basel). 2024 Oct 4;24(19):6422. doi: 10.3390/s24196422.
The aim of this current work is to identify three different gears of cross-country skiing utilizing embedded inertial measurement units and a suitable deep learning model. The cross-country style studied was the skating style during the uphill, which involved three different gears: symmetric gear pushing with poles on both sides (G3) and two asymmetric gears pushing with poles on the right side (G2R) or to the left side (G2L). To monitor the technique, inertial measurement units (IMUs) were affixed to the skis, recording acceleration and Euler angle data during the uphill tests performed by two experienced skiers using the gears under study. The initiation and termination points of the tests were controlled via Bluetooth by a smartphone using a custom application developed with Android Studio. Data were collected on the smartphone and stored on the SD memory cards included in each IMU. Convolutional neural networks combined with long short-term memory were utilized to classify and extract spatio-temporal features. The performance of the model in cross-user evaluations demonstrated an overall accuracy of 90%, and it achieved an accuracy of 98% in the cross-scene evaluations for individual users. These results indicate a promising performance of the developed system in distinguishing between different ski gears within skating styles, providing a valuable tool to enhance ski training and analysis.
本研究旨在利用嵌入式惯性测量单元和合适的深度学习模型，确定三种不同的越野滑雪档位。研究的越野滑雪方式为上坡时的滑冰式，涉及三种不同的档位：双侧撑杆对称档位（G3）和右侧撑杆不对称档位（G2R）或左侧撑杆不对称档位（G2L）。为了监测技术，将惯性测量单元（IMU）固定在滑雪板上，记录两名经验丰富的滑雪者在上坡测试中使用研究档位时的加速度和欧拉角数据。测试的起始和结束点通过智能手机上的蓝牙由定制的 Android Studio 应用程序控制。数据在智能手机上收集并存储在每个 IMU 中包含的 SD 存储卡上。卷积神经网络与长短期记忆相结合，用于分类和提取时空特征。模型在跨用户评估中的性能显示总体准确率为 90%，而在针对个别用户的跨场景评估中准确率达到 98%。这些结果表明，所开发的系统在区分滑冰式不同滑雪档位方面具有良好的性能，为提高滑雪训练和分析提供了有价值的工具。
