Research Progress of Fiber Interferometric-based Vital Signs Monitoring

XU Wei; ZHANG Yeming; NI Jinchao; WANG Xiuyuan; YU Changyuan

doi:10.13756/j.gtxyj.2025.250176

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Research Progress of Fiber Interferometric-based Vital Signs Monitoring

更新时间：2025-12-25

- Research Progress of Fiber Interferometric-based Vital Signs Monitoring
- Study on Optical Communications Issue 6, (2025)
- 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：
- 基金信息：
- DOI：10.13756/j.gtxyj.2025.250176
  CLC： TP212.14
- Received：14 May 2025，
  
  Revised：2025-07-22，
  
  Published：10 December 2025
- 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.

关键词

Keywords

references

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