Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning

Benintendi, Dominic; Tennant, Kevin M.; Sabolsky, Edward M.; Wilhelm, Jay

doi:10.3390/s25154654

Wireless Patch Antenna Characterization for Live Health Monitoring Using Machine Learning

Journal Article · Sun Jul 27 00:00:00 EDT 2025 · Sensors

DOI:https://doi.org/10.3390/s25154654· OSTI ID:2588411

Benintendi, Dominic ^[1]; ^[2]; Sabolsky, Edward M. ^[2]; ^[1]

Ohio University, Athens, OH (United States)
West Virginia University, Morgantown, WV (United States)

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 $$\frac{𝑀𝐻𝑧}{°C}$$. 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.

View Accepted Manuscript (DOE)

Research Organization:: National Energy Technology Laboratory (NETL), Pittsburgh, PA, Morgantown, WV, and Albany, OR (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: FE0032078

OSTI ID:: 2588411

Journal Information:: Sensors, Journal Name: Sensors Journal Issue: 15 Vol. 25; ISSN 1424-8220

Publisher:: MDPI AGCopyright Statement

Country of Publication:: United States

Language:: English

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