Quantitative analysis of thermal recovery using dynamic infrared thermography: a methodological proof-of-concept with
José Marco Balleza Ordaz1,2, Moisés Padilla3, César Daniel Daniel Bravo Alvarado3
1Department of Engineering Physics, Division of Science and Engineering, University of Guanajuato, Campus Leon, Guanajuato, Mexico.
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Objective.To evaluate thermal recovery after controlled cooling using dynamic infrared thermography and to determine whether the recovery process can be described using distinct temporal components associated with different heat transfer dynamics.Approach.A controlled cooling protocol was applied to the plantar region of the foot, followed by thermal recovery monitoring using a radiometric infrared camera. Temperature evolution was analyzed using a two-time-constant exponential model, whereτ1andτ2represent the slow and fast recovery components, respectively. A frame-by-frame radiometric calibration procedure was implemented to ensure measurement stability. The distributions ofτ1andτ2were evaluated under different cooling durations and recovery windows using non-parametric statistics.Main results.Thermal recovery was consistently described by two-time constants that capture distinct temporal regimes. The fast component (τ2) exhibited relatively compact distributions, particularly in the short recovery window, indicating stable and repeatable behavior. In contrast, the slow component (τ1) showed larger variability, especially for longer recovery windows. This variability suggests that the slow component (τ1) is more sensitive to extended thermal dynamics and inter-subject variability than to measurement noise. The results also showed that longer cooling durations increased the relative contribution of the slow recovery component (τ1), revealing multi-scale thermal recovery behavior.Significance.These findings demonstrate that thermal recovery is not a single-scale process but a multi-time dynamic phenomenon in which slow and fast recovery components provide complementary information. By explicitly separating these temporal components, the proposed framework enables a more detailed characterization of thermal dynamics than single-parameter approaches. As a methodological proof-of-concept, this work supports the use of dynamic infrared thermography as a quantitative tool for physiological measurement and establishes a reproducible basis for future clinical studies.
