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嵌套二进制分类器作为人类活动识别系统中的异常检测方法
Agnieszka Duraj1, Daniel Duczymiński1
1Institute of Information Technology, Lodz University of Technology, al. Politechniki 8, 93-590 Łódź, Poland.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种新的嵌套二进制分类器,用于检测人类运动数据中的异常模式. 这种机器学习方法有效地识别异常的身体活动细分,改进人类活动识别 (HAR) 系统.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 人类活动识别 (HAR) 系统需要强大的方法来识别异常的运动模式.
- 区分预期的移动和来自其他活动的异常值对于准确的HAR至关重要.
研究的目的:
- 确定最有效的机器学习方法来检测人类运动数据中的异常细分.
- 提出和评估一种新的嵌套二进制分类器,用于在HAR中检测异常值.
主要方法:
- 该研究将异常值的检测作为分类任务.
- 一个新的嵌套二进制分类器被开发和测试.
- 热门的机器学习算法 (线性回归,SVM,k-NN,决策树) 和深度神经网络被用于比较.
主要成果:
- 嵌套二进制分类器在识别异常模式方面表现出有效性.
- 在三个不同的数据集中使用标准指标和混矩阵来评估性能.
- 拟议的方法在特定情况下显示了与深度神经网络相当或优于深度神经网络的性能.
结论:
- 嵌套二进制分类器是HAR系统中异常模式识别的有效方法.
- 这种方法通过识别异常的人类运动段来提高HAR的可靠性和准确性.
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