用生成对抗网络来识别人类活动的数据增强
IEEE journal of biomedical and health informatics
|February 12, 2024
概括
使用有条件的瓦斯斯坦生成对抗网络 (cWGANs) 生成合成人类活动识别 (HAR) 数据可以提高模型的准确性,特别是在有限的真实数据的情况下. 这种方法提供了一种有效的方式来提高HAR系统的性能.
科学领域:
- 机器学习 机器学习
- 信号处理 信号处理
- 生物医学信息学 生物医学信息学
背景情况:
- 人类活动识别 (HAR) 需要广泛的标记数据来进行概括.
- 获取HAR的标记数据通常是资源密集型和耗时的.
- 生成对抗网络 (GAN) 在HAR中显示出合成数据生成的前景,其性能优于传统增强方法.
研究的目的:
- 引入有条件的瓦斯斯坦生成对抗网络 (cWGANs) 作为生成合成HAR加速度计信号的最佳架构.
- 建立一个强大的方法来评估GAN生成的合成数据的质量和准确性.
- 调查合成数据对不同大小数据集的HAR模型性能的影响.
主要方法:
- 使用1D卷积层用于加速度计信号合成的条件瓦斯斯坦生成对抗网络 (cWGAN) 的实施.
- 计算已建立的信号质量和准确度指标,以评估合成数据.
- 评估将cWGAN生成的数据纳入包含395个用户的大规模HAR数据集的影响.
主要成果:
- 与标准的条件生成对抗网络 (cGANs) 相比,cWGAN架构在加速计信号生成方面表现出更高的性能.
- 将合成数据纳入其中导致较小的真实数据集更显著地提高了性能.
- 观察到需要的合成数据量与可用的真实数据量之间存在反向关系.
结论:
- 条件瓦斯斯坦生成对抗网络 (cWGAN) 提供了一种有效的方法,用于为HAR生成高质量的合成加速度计数据.
- 合成数据生成是一种有价值的策略,可以增加有限的数据集,提高HAR模型的概括性和准确性.
- 拟议的cWGAN方法和评估框架推进了用于HAR应用的合成数据生成领域.
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