从实验室到现实世界:通过个性化评估人类活动识别和优化的有效性
Marija Stojchevska1, Mathias De Brouwer1, Martijn Courteaux1
1IDLab, Ghent University-imec, Technologiepark-Zwijnaarde 82, 9052 Gent, Belgium.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
人类活动识别 (HAR) 模型在现实环境中扎. 转移学习显著提高了使用有限的现实世界数据的新人HAR表现.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 人类活动识别 (HAR) 算法通常在受控环境中进行评估.
- 在现实世界中,HAR面临着诸如噪音,缺失传感器数据和自然人类行为等挑战.
- 现有的HAR模型可能不能很好地概括到各种各样的,未被观察到的日常活动.
研究的目的:
- 为了介绍一个现实世界的HAR开放数据集从一个腕带加速计收集.
- 评估转移学习的有效性,以适应HAR模型适应新的个人和环境.
- 为了突出实验室训练和现实世界HAR模型之间的性能差距.
主要方法:
- 收集了一个新的,未被观察到的,现实世界的HAR数据集,与参与者的自主日常活动.
- 在现实数据集上训练了一个卷积神经网络 (CNN).
- 应用转移学习以使用有限的现实世界数据调整一般CNN模型.
- 在公共 (MHEALTH) 与现实数据集上训练的模型的性能比较.
主要成果:
- 在真实世界数据集上训练的一般CNN模型实现了80%的平均平衡精度 (MBA).
- 转移学习提高了MBA的85%,使用更少的数据.
- 一个仅在MHEALTH数据集上训练的模型实现了100%的MBA,但在现实数据集上下降到62%.
- 将MHEALTH训练的模型与现实世界的数据进行个性化,MBA提高了17%.
结论:
- 转移学习对于将HAR模型适应现实环境和多样化的用户至关重要.
- 现实世界的数据集对于强大的HAR系统开发至关重要.
- 这项研究证明了转移学习的潜力,可以弥合实验室和现实世界HAR表现之间的差距.
关键词:
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