运动分析中的合成数据生成:一个生成的深度学习框架
Mattia Perrone1, Steven P Mell1, John T Martin1
1Section of Young Adult Hip Surgery, Division of Sports Medicine, Department of Orthopedic Surgery, Rush Medical College of Rush University, Rush University Medical Center, Chicago, IL, USA.
概括
生成式深度学习有效地增加了使用变化自动编码器的运动分析数据. 这些合成数据改善了生物力学预测,在联合时刻分析中显示了与真实数据相比的准确性.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 运动分析经常面临数据稀缺,限制了模型训练.
- 生成式深度学习为数据增强提供了解决方案.
- 变量自编码器 (VAE) 是合成数据生成的强大工具.
研究的目的:
- 引入基于VAE的数据增强策略,用于运动分析.
- 为了生成合成的动力学和动力学变量.
- 评估增强数据在改善生物力学预测方面的有效性.
主要方法:
- 使用变化自编码器生成合成动力学 (联合角度) 和动力学 (联合时刻,地面反应力) 数据.
- 使用统计参数映射 (SPM) 来比较真实和合成数据分布.
- 在真实数据 (R) 和结合真实和合成数据 (R&S) 上训练了一种长期短期记忆 (LSTM) 模型,用于性能评估.
主要成果:
- SPM证实真实和合成生物机械数据之间没有显著差异.
- 在R&S上训练的LSTM模型与仅在R.上训练的模型相比,实现了可比或更高的正常化根平均平方误差 (nRMSE).
- 在预测膝关节和关节在正面和斜腰平面上的关节时刻方面,注意到了具体的改进.
结论:
- 基于VAE的数据增强对于运动分析是有效的,特别是当真实数据有限时.
- 由VAE生成的合成数据可以提高像LSTM这样的预测模型的性能.
- 这种方法为增强生物力学数据集提供了一种新且高效的方法.
相关概念视频
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