双流LSTM网络中的多模组序列动态和融合优化,用于复杂的生理状态估计
1Department of Public Physical Education, China Academy of Art, Hangzhou, Zhejiang, China.
Frontiers in neurorobotics
|February 23, 2026
概括
本研究介绍了基于注意力的双流长期短期记忆 (DS-LSTM) 网络,用于个性化的排球训练. 该模型增强了多模式序列建模,用于准确的训练状态估计和反.
科学领域:
- 运动科学 运动科学 运动科学
- 人工智能的人工智能
- 生物机械工程 生物机械工程
背景情况:
- 个性化体育训练需要精确建模复杂的动力学和生理学数据.
- 当前的方法面临的挑战是融合不稳定性和多式联络序列建模中的特征错位.
研究的目的:
- 为科学化和个性化排球体育训练开发一个动态的框架.
- 为了解决融合不稳定性和多模式序列建模中的特征错位问题.
主要方法:
- 提出了一个双流长期短期记忆 (DS-LSTM) 网络,集成了一个时间注意力机制.
- 该框架将异质特征学习脱,并优化了多模式序列的时间重量分布.
主要成果:
- 在复杂运动状态估计中,负载建模误差降至3.8%.
- 实现了93.1%的运动分类准确度和0.91.1%的速度轨迹适配系数的确定.
- 证明了0.05m/s的峰值速度轨迹偏差.
结论:
- 基于注意力的DS-LSTM有效地优化了多模式序列建模用于训练状态估计.
- 拟议的框架增强了排球体育训练的个性化和科学化.
- 在复杂的运动状态估计和反系统中得到验证的有效性.
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