对于体育运动运动识别的深度学习,具有高精度的性能增强框架
Yang Yang1, Fallah Mohammadzadeh2, Mohammad Khishe3
1School of Physical Education, Suzhou University, Suzhou, Anhui, China.
Scientific reports
|November 6, 2025
概括
这项研究介绍了一个具有波形变换的进化并行循环网络 (EPRN),用于更优质的运动运动识别. EPRN模型显著提高了准确性和稳定性,在性能分析和伤害预防方面表现优于传统的深度学习方法.
科学领域:
- 运动科学 运动科学 运动科学
- 生物机械工程 生物机械工程
- 人工智能的人工智能
背景情况:
- 传统的深度学习模型,如LSTM和变压器,由于长期的依赖和输入噪声,在准确识别运动运动方面面临挑战.
- 有效的运动运动识别对于运动员表现分析,伤害预防和监测至关重要.
研究的目的:
- 为了开发一种新的深度学习框架,Evolved Parallel Recurrent Network (EPRN) 与波形变换集成,用于高精度的体育运动运动识别.
- 解决现有模型在捕捉复杂的运动动态和噪声方面的局限性.
主要方法:
- 提出了一个进化的并行循环网络 (EPRN) 架构,具有用于增强时间建模的并行循环路径.
- 实现基于波纹的特征提取,以在多个分辨率中保存细粒度的运动细节.
- 在基准运动运动数据集上评估了EPRN模型,并将其性能与LSTM,GRU和CNN模型进行了比较.
主要成果:
- 与其他架构相比,EPRN模型表现出卓越的性能,根平均平方误差 (RMSE) 减少了23.5%,结构相似度指数 (SSIM) 增加了12.7%.
- 剩余分析表明,EPRN表现出较低的错误可变性和对突然运动过渡的敏感性降低,这意味着增强的稳定性.
- 基于波形变换的特征提取和循环深度学习的结合显著提高了运动识别的准确性.
结论:
- 进化并行循环网络 (EPRN) 与传统的深度学习模型相比,为体育运动运动识别提供了更准确和更强大的解决方案.
- 这种方法对现实生活中的应用有很大的潜力,包括体育表现分析,实时运动跟踪和康复系统.
- 未来的研究方向包括多式联网数据融合和实时应用轻量级EPRN变体的开发.
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