干涉型光纤生命体征传感监测技术的研究综述

徐伟; 张业铭; 倪锦超; 王修远; 余长源

doi:10.13756/j.gtxyj.2025.250176

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干涉型光纤生命体征传感监测技术的研究综述

专题：纪念创刊50周年 | 更新时间：2025-12-25

- 干涉型光纤生命体征传感监测技术的研究综述
- Research Progress of Fiber Interferometric-based Vital Signs Monitoring
- 光通信研究 2025年第6期
- 作者机构：
  
  1.苏州工学院　电子信息工程学院，苏州　215500
  2.香港理工大学　电子及资讯工程学系，香港　999077
- 作者简介：
  
  徐伟（1986-），男，江苏常熟人。讲师，博士，主要研究方向为新型光纤集成器件和算法。
  张业铭（2003-），男，江苏泰州人。本科，主要研究方向为光纤传感技术。
  余长源，教授。E-mail：changyuan.yu@polyu.edu.hk
- 基金信息：
  
  苏州市科技局资助项目(2022SS03);香港理工大学资助项目(1-WZ01;1-CD8N)
- DOI：10.13756/j.gtxyj.2025.250176
  中图分类号： TP212.14
- 收稿：2025-05-14，
  
  修回：2025-07-22，
  
  纸质出版：2025-12-10
- 稿件说明：
移动端阅览
徐伟，张业铭，倪锦超，等. 干涉型光纤生命体征传感监测技术的研究综述[J]. 光通信研究，2025(6): 250176.

Xu W, Zhang Y M, Ni J C, et al. Research Progress of Fiber Interferometric-based Vital Signs Monitoring[J]. Study on Optical Communications, 2025(6): 250176.
徐伟，张业铭，倪锦超，等. 干涉型光纤生命体征传感监测技术的研究综述[J]. 光通信研究，2025(6): 250176. DOI： 10.13756/j.gtxyj.2025.250176.

Xu W, Zhang Y M, Ni J C, et al. Research Progress of Fiber Interferometric-based Vital Signs Monitoring[J]. Study on Optical Communications, 2025(6): 250176. DOI： 10.13756/j.gtxyj.2025.250176.

摘要

文章回顾了研究小组在抗失真及人工智能（AI）技术在干涉型光纤生命体征传感监测中的研究进展。文章从光纤马赫-曾德尔干涉仪（MZI）的信号失真问题出发，分析了动圈式相位调制、生成对抗网络（GAN）神经网络重建、薄膜压电辅助解调及垂直腔面发射激光器（VCSEL）有源零差解调等抗失真技术；同时探讨了ELA模型和改进U-Net等端到端AI算法在心率（HR）、呼吸频率（RR）监测及心冲击（BCG）信号特征提取中的应用。这些技术进一步完善了非侵入式光纤干涉型生命体征监测系统，为其在大健康领域大范围应用起了重要的支撑作用。

Abstract

This paper reviews our progress in applying anti-distortion and Artificial Intelligence (AI) techniques to fiber-optic interferometric vital signs monitoring. To solve the signal distortion issues in fiber Mach-Zehnder Interferometers (MZI)

several distortion-resistant methods have been proposed

including moving-coil transducer

Generative Adversarial Network (GAN)-based neural network reconstruction

thin-film piezoelectric-assisted demodulation

and Vertical Cavity Surface Emitting Laser (VCSEL)-based active homodyne demodulation. Meanwhile

the applications of end-to-end AI algorithms have also been explored for Heart Rate (HR)

Respiratory Rate (RR) monitoring

and feature extraction in Ballistocardiogram (BCG) signals

such as the ELA model and improved U-Net. These technologies have further improved the non-invasive fiber-optic interferometric vital sign monitoring system

playing a crucial supporting role in the wide application of this system in the field of general health.

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references

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