地反应力曲线的时间规范化和生物机械信号处理技术对深度学习模型性能的影响
Oussama Jlassi1, Philippe C Dixon1
1Department of Kinesiology and Physical Activity, McGill University, Montreal, Québec, Canada.
Journal of biomechanics
|April 27, 2024
概括
预处理时间序列的生物力学数据会影响深度学习模型的性能. 使用零填充和min-max缩放的未过数据为卷积神经网络和长短期记忆模型带来了最高的分类准确性.
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:
- 时间序列数据在生物力学研究中很普遍.
- 在分析之前,时间规范化和过等预处理步骤是常见的.
- 这些预处理步骤对深度学习模型性能的影响尚不清楚.
研究的目的:
- 评估时间规范化和过如何影响深度学习分类模型的性能.
- 研究振幅缩放对模型结果的影响.
- 确定生物力学时间序列数据的最佳预处理策略.
主要方法:
- 利用了古登堡步行数据库,其中包含350名健康个体的3D地面反应力.
- 训练有素的卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 模型用于二元性别分类.
- 应用了各种预处理技术:零填充,插值,过和振幅缩放 (min-max,体质).
主要成果:
- 预处理转换显著改变了模型性能.
- 没有过的数据,零填充和最小-最大幅度缩放实现了最高的F1得分 (91%为CNN,87%为LSTM).
- 排除过和应用最小-最大缩放通常在两个架构中提高了性能;插值结果各不相同.
结论:
- 预处理选择极大地影响生物力学中的深度学习分类准确性.
- 避免过和使用最小-最大缩放等特定方法似乎是有益的.
- 进一步的研究应该考虑这些预处理效应可靠的深度学习应用程序.
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