滑冰中的装备分类 越野滑雪使用惯性传感器和深度学习
Antonio Pousibet-Garrido1, Aurora Polo-Rodríguez2, Juan Antonio Moreno-Pérez1
1ECsens, 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.
Sensors (Basel, Switzerland)
|October 16, 2024
概括
本研究使用惯性测量单元和深度学习来识别越野滑雪滑冰装备. 该系统准确地区分对称和不对称的杆推技术,增强滑雪训练.
科学领域:
- 运动科学 运动科学 运动科学
- 生物机械分析 生物机械分析
- 运动中的机器学习
背景情况:
- 越野滑雪,特别是滑冰风格,涉及复杂的技术.
- 准确识别不同的滑雪装备对于性能优化和伤害预防至关重要.
- 现有的技术分析方法可能是繁的或缺乏精度.
研究的目的:
- 开发和验证一种用于识别越野滑雪滑冰三种不同的装备的系统.
- 利用嵌入式惯性测量单元 (IMU) 和深度学习用于自动化技术分类.
- 为增强滑雪训练和性能分析提供一个工具.
主要方法:
- 两名经验丰富的滑雪运动员使用对称 (G3) 和不对称 (G2L,G2R) 杆推轮进行了上坡滑冰技术.
- 惯性测量单元 (IMU) 连接到滑雪板上以记录加速和欧勒角数据.
- 卷积神经网络 (CNN) 与长期短期记忆 (LSTM) 网络相结合,用于时空特征提取和分类.
主要成果:
- 开发的深度学习模型在跨用户评估中实现了90%的准确性.
- 该系统在对个人用户的交叉场景评估中显示了98%的高准确性.
- 该模型有效地区分了对称 (G3) 和不对称 (G2L,G2R) 滑雪装备.
结论:
- 该系统显示出在滑冰技术中准确识别不同滑雪装备的显著前景.
- IMU和深度学习的整合为滑雪训练和生物机械分析提供了有价值的,非侵入性的工具.
- 这项技术可以帮助教练和运动员改进技术并提高表现.
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