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通过卷积特征提取器块和基于矢量的相对位置嵌入来增强变压器模型,用于人类活动识别

Xin Guo¹, Young Kim¹, Xueli Ning¹

¹Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.

Sensors (Basel, Switzerland)

|January 25, 2025

概括

这项研究使用IMU传感器增强了用于人类活动识别 (HAR) 的变压器模型. 新的方法可以更好地捕捉时间相关性,从而提高HAR的性能.

科学领域:

计算机科学计算机科学
人工智能的人工智能
信号处理信号处理

背景情况:

变压器模型在人类活动识别 (HAR) 中很受欢迎,因为它们能够捕捉远程依赖.
现有的变压器模型很难有效地利用惯性测量单元 (IMU) 传感器数据中的复杂时间相关性.

研究的目的:

通过更好地利用IMU传感器数据中的时间相关性来增强HAR的变压器模型.
引入新的组件,以提高模型捕获本地和全球时间序列特征和相对位置信息的能力.

主要方法:

提出了一个卷积特征提取器块 (CFEB),使用多层卷积层来捕获本地和全球时间序列特征.
引入基于矢量的相对位置嵌入 (vRPE) 以更好地表示与绝对位置嵌入 (APE) 相比的时间位置相关性.
在三个基准HAR数据集上对增强的变压器模型进行了评估:KU-HAR,UniMiB SHAR和USC-HAD.

主要成果:

拟议的CFEB和vRPE显著提高了变压器模型在HAR任务中的性能.
改进后的模型在利用IMU传感器时间序列信号中的复杂时间相关性方面表现出卓越的能力.
实验结果证实,在多个HAR数据集中,性能大大提高.

结论:

CFEB和vRPE的整合为基于变压器的HAR提供了一个有效的增强方案.
拟议的方法使变压器模型能够更好地利用来自IMU传感器数据的先验信息来改进活动分类.
这项工作为使用深度学习模型推进HAR系统提供了一个有希望的方向.

关键词:

卷积神经网络 (CNN) 是一种神经网络.人类活动的认可人类活动的认可惯性测量单位 (IMU) 是指惯性测量单位.相对位置嵌入式嵌入式时间序列信号时间序列信号.变压器模型变压器模型

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