从头部上的惯性传感器对人类活动进行开放式识别
Angela Cortese1, Sarah Solbiati1, Alice Scandelli1
1Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
本研究引入了使用可穿戴传感器进行人类活动识别 (HAR) 的开放式识别框架. 它有效地识别了未知的活动,提高了基于传感器的系统的真实可靠性.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 使用惯性测量单位 (IMU) 的人类活动识别 (HAR) 对医疗保健和健身至关重要.
- 由于存在未见的活动 (封闭集限制),现有的HAR方法与现实世界的场景作斗争.
研究的目的:
- 通过实施开放式集识别 (OSR) 框架,使HAR分类器适应现实世界的应用.
- 用智能眼镜IMU数据评估OSR在识别未知活动方面的有效性.
主要方法:
- 在六轴IMU数据上训练了一个轻量级的1D卷积神经网络.
- 在一个广泛的评估中,在两个数据集 (专有数据和UCA-EHAR) 上使用了一个离开一个活动的协议.
- 通过使用logit空间方法评估开放式HAR性能,例如GMM,KDE,OpenMax和NNdr.
主要成果:
- 在ROC曲线下的面积 (AUC) 大于0.8.8的情况下,可以对未知活动进行可靠的识别.
- 该OSR框架以最小的计算开销证明了它的有效性.
- 该研究验证了专有数据和公共数据集 (UCA-EHAR) 的发现.
结论:
- 低复杂性的OSR方法在资源有限的可穿戴设备上具有实时 HAR 的巨大潜力.
- 这项研究支持开发适应性和可靠的基于传感器的识别系统,用于实际应用.
- 这些发现为更强大的HAR系统铺平了道路,能够处理新型活动.
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