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Related Experiment Video

Updated: Feb 28, 2026

Infrared Thermography for the Detection of Changes in Brown Adipose Tissue Activity
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Machine Learning Calibration of Smartphone-Based Infrared Thermal Cameras: Improved Bias and Persistent Random Error.

Jayroop Ramesh1, Tom Loney2, Stefan Du Plessis2

  • 1Department of Computer Science and Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

Smartphone thermal cameras like the FLIR One Pro show significant bias and poor agreement for absolute skin temperature measurement compared to standard thermometers. Algorithmic correction cannot fully overcome their inherent limitations for clinical use.

Keywords:
bland-altmancalibrationmachine learningskin temperaturesmartphone plug-in camerasthermal imagesthermography

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Area of Science:

  • Biomedical Engineering
  • Medical Instrumentation
  • Thermal Imaging

Background:

  • Smartphone thermal cameras offer accessible physiological monitoring.
  • Their accuracy for absolute skin temperature measurement in clinical settings is debated.

Purpose of the Study:

  • To compare the agreement and repeatability of the FLIR One Pro smartphone thermal camera against the iHealth PT3 non-contact infrared thermometer.
  • To assess the effectiveness of machine learning for calibrating FLIR One Pro readings.

Main Methods:

  • A method comparison study involving 40 healthy adults and 2400 temperature measurements.
  • Concurrent skin temperature measurements of the hand dorsum using FLIR One Pro and iHealth PT3.
  • Paired t-tests and Bland-Altman analysis for agreement and repeatability assessment.

Main Results:

  • The iHealth PT3 showed superior precision (SD ≈ 0.03-0.09 °C) compared to the FLIR One Pro (SD ≈ 0.30-0.34 °C).
  • FLIR One Pro exhibited substantial mean bias (-1.15 to -1.42 °C) and wide limits of agreement (≈6 °C).
  • Machine learning calibration reduced bias but did not fully compensate for the FLIR One Pro's high variability (R² = 0.152).

Conclusions:

  • The FLIR One Pro has significant limitations for absolute skin temperature measurement due to bias and variability.
  • Algorithmic correction is insufficient to overcome the device's fundamental measurement constraints.
  • The device has limited utility for clinical monitoring or diagnostic decisions requiring precise temperature thresholds.