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注意增强的卷积BiLSTM模型用于预测体育伤害中恢复结果
Annapoorani Chandrasekarapuram Lakshminarayanan1, Jayasree Thandavamoorthi2
1Department of Biomedical Engineering, Chennai Institute of Technology, Kundrathur, Chennai - 600069, Tamil Nadu India.
Indian journal of orthopaedics
|February 11, 2026
概括
一个新的混合深度学习模型,以注意力为基础的随机森林优化卷积双向长期短期记忆 (A-RF-CBiLSTM),显著改善了体育受伤评估和下肢受伤康复预测.
科学领域:
- 生物力学 生物力学
- 深度学习 (Deep Learning) 是一种深度学习.
- 运动医学 运动医学
背景情况:
- 有效管理体育伤害和康复对于体育表现至关重要,尤其是年轻运动员.
- 下肢受伤很常见,并显著影响移动性,需要个性化康复.
- 传统的生物机械分析与复杂的人类运动数据作斗争,限制了伤害评估和恢复预测.
研究的目的:
- 引入一种新的混合深度学习模型,用于增强体育伤害评估.
- 改善预测下肢损伤的康复结果.
- 克服传统生物机械分析在捕获运动数据方面的局限性.
主要方法:
- 开发了一个基于注意力的随机森林优化卷积双向长期短期记忆 (A-RF-CBiLSTM) 模型.
- 利用多个数据集,包括EMG和动力学数据进行全面分析.
- 采用随机森林来进行特征选择,CBAM用于注意,CNN用于特征提取,BiLSTM用于时间依赖分析.
主要成果:
- A-RF-CBiLSTM模型实现了高性能指标:98.87%的准确性,98.60%的精度,97.34%的回忆,97.34%的F1得分,97.88%的特异性.
- 在预测康复结果和识别下肢损伤模式方面表现出有效性.
- 通过广泛的性能评估验证了模型的稳定性和可靠性.
结论:
- 拟议的A-RF-CBiLSTM模型代表了体育伤害管理的生物力学分析的重大进步.
- 这种混合深度学习方法为伤害评估和康复预测提供了强大而高效的解决方案.
- 这些发现突出了先进的人工智能技术在优化运动恢复和表现方面的潜力.
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