# Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning

## TL;DR

This paper introduces wireless patch antennas combined with machine learning to monitor temperatures in harsh environments like coal-fired power plants.

## Contribution

A novel wireless patch antenna system with LSTM-based calibration for improved non-contact temperature monitoring in extreme environments.

## Key findings

- Wireless patch antennas showed sensitivities between 0.052 to 0.20 MHz°C for temperature estimation.
- LSTM-based calibration reduced temperature estimation error by up to 76% compared to Linear Regression.
- The system outperforms conventional methods like RFID, infrared, and thermocouples in non-contact temperature monitoring.

## Abstract

Temperature monitoring in extreme environments, such as coal-fired power plants, was addressed by designing and testing wireless patch antennas for use in machine learning-aided temperature estimation. The sensors were designed to monitor the temperature and health of boiler systems. Wireless interrogation of the sensor was performed using a Vector Network Analyzer (VNA) and a pair of interrogation antennas to capture resonance behavior under varying thermal and spatial conditions with sensitivities ranging from 0.052 to 0.20 MHz°C. Sensor calibration was conducted using a Long Short-Term Memory (LSTM) model, which leveraged temporal patterns to account for hysteresis effects. The calibration method demonstrated improved performance when combined with an LSTM model, achieving up to a 76% improvement in temperature estimation error when compared with Linear Regression (LR). The experiments highlighted an innovative solution for patch antenna-based non-contact temperature measurement, which addresses limitations with conventional methods such as RFID-based systems, infrared, and thermocouples.