论文标题
基于WiFi渠道状态信息的人类生物特征信号使用深度学习
Human Biometric Signals Monitoring based on WiFi Channel State Information using Deep Learning
论文作者
论文摘要
在本文中，我们首先提出了一个单输入，多输出卷积神经网络，该网络可以通过利用心率和呼吸率之间的基本联系来同时估算心率和呼吸率。神经网络的输入是一对WiFi设备收集的通道状态信息的幅度和阶段。我们基于WiFi的技术解决了隐私问题，并且适合各种环境。该系统的心脏和呼吸率估计的总体准确性分别可以达到99.109％和98.581％。此外，我们开发并分析了两种基于深度学习的神经网络分类算法，用于对四种类型的睡眠阶段进行分类：唤醒，快速眼动（REM）睡眠，非比型眼动运动（NREM）轻度睡眠和NREM深度睡眠。该系统总体分类精度可以达到95.925％
In this paper, we first present a single-input, multiple-output convolutional neural network that can estimate both heart rate and respiration rate simultaneously by exploiting the underlying link between heart rate and respiration rate. The inputs to the neural network are the amplitude and phase of channel state information collected by a pair of WiFi devices. Our WiFi-based technique addresses privacy concerns and is adaptable to a variety of settings. This system overall accuracy for the heart and respiration rate estimation can reach 99.109% and 98.581%, respectively. Furthermore, we developed and analyzed two deep learning-based neural network classification algorithms for categorizing four types of sleep stages: wake, rapid eye movement (REM) sleep, non-rapid eye movement (NREM) light sleep, and NREM deep sleep. This system overall classification accuracy can reach 95.925%
