DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing

Sensors (Basel). 2024 Apr 23;24(9):2672. doi: 10.3390/s24092672.

Abstract

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach-Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.

Keywords: DEMA; EMD; LSTM; MZI; optical fiber sensor; vital signs monitoring.

MeSH terms

  • Deep Learning*
  • Humans
  • Monitoring, Physiologic / instrumentation
  • Monitoring, Physiologic / methods
  • Neural Networks, Computer
  • Optical Fibers*
  • Vital Signs* / physiology