人类运动生物力学时间序列数据的数据增强:一个范围审查
Christina Halmich1,2, Lucas Höschler3, Christoph Schranz2
1Department of Artificial Intelligence and Human Interfaces, Paris-Lodron-University Salzburg, Salzburg, Austria.
PloS one
|July 1, 2025
概括
生物力学中的数据增强对于改善机器学习模型至关重要,尽管数据有限. 目前的方法需要更好的评估,特别是关于现实的运动模拟和对模型性能的影响.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
- 数据科学数据科学数据科学
背景情况:
- 机器学习和深度学习提供先进的生物力学数据分析.
- 挑战包括有限的数据,高成本,传感器可变性和运动模式多样性.
- 数据增强是一个潜在的解决方案,但其在生物力学时间序列数据中的应用需要进一步评估.
研究的目的:
- 对生物力学时间序列数据的数据增强技术进行范围审查.
- 检查当前的方法,评估其有效性,并提供应用建议.
主要方法:
- 在四个数据库 (PubMed,IEEE Xplore,Scopus,Web of Science) 进行了系统的文献搜索.
- 包括在2013年至2024年间发表的研究.
- 在研究选择过程中遵循了PRISMA-ScR指南.
主要成果:
- 确定了21个相关出版物.
- 对于数据增强,没有单一的最佳实践;方法取决于研究.
- 关键问题包括在合成数据中缺乏软组织工件,以及对增强方法对下游模型影响的评估不足.
结论:
- 数据增强对于克服数据稀缺性和提高生物力学模型概括性至关重要.
- 根据特定数据特征定制增强技术对于提高预测模型性能至关重要.
- 需要进一步的研究,以了解各种增强技术对数据质量和模型结果的影响.
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