用廉价Wi-Fi设备和AI实现无感心肺监测，精度媲美高价多天线系统
PulseFi: A Low Cost Robust Machine Learning System for Accurate Cardiopulmonary and Apnea Monitoring Using Channel State Information
- 通过分析Wi-Fi信道状态信息（CSI）构建轻量LSTM模型
- 在118人数据集上实现与多天线系统相当的心率/呼吸率监测精度
- 适合居家健康监测、远程医疗等低成本场景
非侵入式生命体征监测在医疗场景中日益重要。本文提出PulseFi，一种基于商用低成本设备的新型无感监测系统，利用Wi-Fi传感与人工智能技术，连续准确监测心率、呼吸率并检测呼吸暂停事件。该系统采用信号处理流水线处理来自通道状态信息（CSI）的无线电信号，并输入定制的低计算量长短期记忆（LSTM）神经网络模型。我们在两个数据集上评估了PulseFi：一个由ESP32设备本地采集，另一个包含118名受试者使用Raspberry Pi 4B采集的记录，后者为同类研究中最全面的数据集。结果表明，PulseFi能以无缝无感的方式有效估计心率与呼吸率，精度可与昂贵且难以普及的多天线系统相媲美甚至更优。
原文摘要 · Abstract (English)
Non-intrusive monitoring of vital signs has become increasingly important in a variety of healthcare settings. In this paper, we present PulseFi, a novel low-cost non-intrusive system that uses Wi-Fi sensing and artificial intelligence to accurately and continuously monitor heart rate and breathing rate, as well as detect apnea events. PulseFi operates using low-cost commodity devices, making it more accessible and cost-effective. It uses a signal processing pipeline to process Wi-Fi telemetry data, specifically Channel State Information (CSI), that is fed into a custom low-compute Long Short-Term Memory (LSTM) neural network model. We evaluate PulseFi using two datasets: one that we collected locally using ESP32 devices and another that contains recordings of 118 participants collected using the Raspberry Pi 4B, making the latter the most comprehensive data set of its kind. Our results show that PulseFi can effectively estimate heart rate and breathing rate in a seemless non-intrusive way with comparable or better accuracy than multiple antenna systems that can be expensive and less accessible.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。