通过可穿戴设备减少对人类活动识别的标签依赖:从监督学习到创新的弱自主监督方法
1Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究探讨了使用可穿戴传感器的人类活动识别 (HAR),并比较了各种机器学习方法. 一个新的弱自主监督框架以最小的标记数据实现了高精度,为HAR应用提供了实用的解决方案.
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:
- 使用可穿戴传感器的人类活动识别 (HAR) 面临着性能标记的权衡.
- 完全监督的方法需要广泛的,昂贵的标记数据.
- 无监督的方法减少了标签,但通常会产生更低的性能.
研究的目的:
- 调查基于可穿戴设备的HAR监督的全部范围.
- 专注于新的方法,尽量减少标签,同时保持准确性.
- 比较传统的监督,无监督,弱监督,多任务,自我监督和弱自我监督的学习.
主要方法:
- 开发并比较了六种学习模式:完全监督,无监督,弱监督,多任务,自我监督和一个新的弱自我监督框架.
- 在拟议的框架中,利用领域知识和最小的标记数据.
- 对基于可穿戴的 HAR 的基准数据集进行了实验.
主要成果:
- 监管较弱的方法实现了与监管减少的完全监管方法相比的性能.
- 多任务框架通过知识共享提高了业绩.
- 这种新的弱自主监督方法使用仅10%的标记数据显示出高效率.
结论:
- 不同的学习范式为 HAR 提供了基于数据可用性的互补优势.
- 新的弱自监管框架是有限的标记数据的实际HAR的有希望的解决方案.
关键词:
深度学习是一种深度学习.人类活动的认可 人类活动的认可机器学习是机器学习.多任务学习是多任务学习.神经网络的神经网络的神经网络代表性学习学习学习自主监督学习学习一个无处不在的计算.缺乏监督的学习学习.可穿戴式传感器数据数据更多相关视频
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