基于GWO-BPNN深度学习的特征数据融合的滑雪手套的抓地力研究
Xiping Ma1, Xinghua Gao2, Yixin Zhang2
1School of Electrical and Information Engineering, Beihua University, Jilin 132021, China.
Sensors (Basel, Switzerland)
|December 11, 2025
概括
这项研究开发了一种混合深度学习模型,以使用灵活的传感器准确测量滑雪杆抓地力. 灰狼优化器反向传播神经网络 (GWO-BPNN) 实现了高精度,帮助智能滑雪手套的开发.
科学领域:
- 生物力学 生物力学
- 运动技术技术 运动技术
- 机器学习 机器学习
背景情况:
- 在曲的滑雪杆表面上准确测量抓地力是具有挑战性的.
- 现有的方法与动态的力量分配和不同的手姿势扎.
- 开发精确的抓地力预测模型对于滑雪性能分析至关重要.
研究的目的:
- 为了研究滑雪杆抓住时特有的压力分布模式.
- 开发一个高精度的抓地力预测模型,用于滑雪.
- 为了减少因动态条件和姿势变化引起的力测量误差.
主要方法:
- 使用多点灵活阵列传感器来检测关键接触点的力.
- 采用深度神经网络 (DNN) 训练来提取握力数据.
- 开发了一种混合方法,将DNN与灰狼优化 (GWO) 算法 (GWO-BPNN) 结合起来,用于数据融合和优化.
- 从校准的传感器数据构建了一个全面的数据库.
主要成果:
- 与校准实验相比,GWO-BPNN模型的相对误差低于2%.
- 在力测量中显著提高了灵活传感器应用的准确性.
- 成功地应用了该模型来开发智能滑雪手套.
结论:
- 拟议的GWO-BPNN模型为准确的滑雪杆抓地力测量提供了强大的解决方案.
- 这项技术为滑雪中的绩效指导和评估提供了科学基础.
- 开发的智能滑雪手套可以增强运动员的训练和反机制.
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