DNN-Assisted activity classification using fiber interferometer sensor

Guohao Zhu, Wei Xu, Cheung Chuen Yu, Wenye Sun, Bo Dong, Changyuan Yu, Wei Zhao, Yishan Wang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


Deep Neural Network (DNN) assisted activity monitoring algorithms are investigated, aiming to discriminate three activity states, including presence without movement, nobody in bed, and presence with movement. The signal is collected from a fiber-based Mach-Zehnder Interferometer (MZI) sensor, which is placed under a 20-cm thick mattress. When people are lying on the mattress, cardiopulmonary activities will lead to the change of the phase difference of the MZI optical fiber sensor. In this paper, three kinds of DNNs are developed to investigate the classification performance, including feedforward neural network (FNN), convolutional neural network (CNN), and long short-Term memory network (LSTM). The accuracy of FNN, CNN and LSTM is 95.14%, 99.01%, and 99.37% within one second, respectively. Moreover, LSTM has low time and space complexity and better performance. The algorithms constructed can obtain high accuracy and robustness with low computational overhead and storage consumption and have broad application prospects. What's more, the MZI optical fiber sensor has many advantages such as low cost and anti-electromagnetic interference, which means that the system can be popular in medical treatment and households.

Original languageEnglish
Title of host publicationOptoelectronic Devices and Integration X
EditorsXuping Zhang, Baojun Li, Changyuan Yu, Xinliang Zhang
ISBN (Electronic)9781510646377
Publication statusPublished - Oct 2021
EventOptoelectronic Devices and Integration X 2021 - Nantong, China
Duration: 10 Oct 202112 Oct 2021

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceOptoelectronic Devices and Integration X 2021


  • Activity Monitoring
  • CNN
  • DNN
  • FNN
  • LSTM
  • MZI

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'DNN-Assisted activity classification using fiber interferometer sensor'. Together they form a unique fingerprint.

Cite this