基于毫米波深度传感的抗伴生干扰的眨眼检测
Anti-accompanying interference blink detection using deep millimeter wave sensing
荆楠 1刘冠男 1张楠 1王林2
作者信息
- 1. 燕山大学信息科学与工程学院 秦皇岛 066004;河北省信息传输与信号处理重点实验室 秦皇岛 066004
- 2. 燕山大学信息科学与工程学院 秦皇岛 066004
- 折叠
摘要
眨眼检测在多种实际应用场景中起着关键作用,如眼病检测、人机交互、疲劳驾驶预防等.针对来自人体的伴生干扰会严重影响眨眼信号的特征提取问题,本文提出一种非线性独立分量分析框架的自监督深度对比学习方法来分离眨眼和伴生干扰.本文设计一个基于时间相关性的分离网络ES-Net1,该网络将具有时间相关和时间不相关的两个正负样本序列作为网络的输入,通过ES-Net1 内部的特征提取器恢复眨眼和伴生干扰信号的时间结构,从而实现非线性混合信号的分离.本文基于TI公司的AWR1642 毫米波雷达平台实现mmBlinkSEN原型系统,通过14 000 组数据验证mmBlinkSEN的有效性.实验结果表明,在存在人体伴生干扰情况下,mmBlinkSEN对眨眼频率的检测精度高达 88%.
Abstract
Blink detection is crucial in various practical application scenarios,such as eye disease detection,human-computer interaction,fatigued driving prevention,etc.To address the serious effect on the extraction of blink signal from the accompanying interference induced of the human body's micro-scale movement,we propose a blink detection system,mmBlinkSEN,which can overcome the effects of accompanying interference and recover the blink waveform effectively.Inspired by the fact that blink and accompanying interference are mixed in a non-linear manner,a self-supervised deep contrastive learning method with a non-linear independent component analysis framework is proposed to separate blink and accompanying interference.A separation network ES-Net1 is designed,which is based on temporal correlation.The network takes two positive and negative sample sequences with temporal correlation and temporal uncorrelation as input to the network.The internal feature extractor inside the ES-Net1 is utilized to recover the temporal structure of the blink and the accompanying interference signal.Thus,the separation of the non-linear mixed signal is achieved.This article implements the mmBlinkSEN prototype system based on TI's AWR1642 millimeter wave radar platform and validates the effectiveness of mmBlinkSEN with 14,000 sets of data.Experimental results show that mmBlinkSEN detects blink frequency with up to 88%accuracy in the presence of accompanying human interference.
关键词
眨眼检测/伴生干扰/深度对比学习/毫米波雷达/非线性独立分析Key words
blink detection/accompanying interference/deep contrastive learning/millimeter wave radar/nonlinear independent component analysis引用本文复制引用
基金项目
河北省自然科学基金(F2022203045)
河北省科技厅中央引导地方科技发展专项(236Z0801G)
河北省重点实验室项目(202250701010046)
河北省重点实验室项目(22567637H)
出版年
2024
NETL
NSTL
万方数据