Counterpoint to “Infrared cameras overestimate skin temperature during rewarming from cold exposure”

Predicting Deep Body Temperature (Tb) from Forehead Skin Temperature: Tb or Not Tb?

There is a need to rapidly screen individuals for heat strain and fever using skin temperature (T_sk) as an index of deep body temperature (T_b). This study’s aim was to assess whether T_sk could serve as an accurate and valid index of T_b during a simulated heatwave. Seven participants maintained a continuous schedule over 9-days, in 3-day parts; pre-/post-HW (25.4 °C), simulated-HW (35.4 °C). Contact thermistors measured T_sk (T_forehead, T_finger); radio pills measured gastrointestinal temperature (T_gi). Proximal-distal temperature gradients (ΔT_{forehead–finger}) were also measured. Measurements were grouped into ambient conditions: 22, 25, and 35 °C. T_gi and T_forehead only displayed a significant relationship in 22 °C (r: 0.591; p < 0.001) and 25 °C (r: 0.408; p < 0.001) conditions. A linear regression of all conditions identified T_forehead and ΔT_{forehead–finger} as significant predictors of T_gi (r²: 0.588; F: 125.771; p < 0.001), producing a root mean square error of 0.26 °C. Additional residual analysis identified T_forehead to be responsible for a plateau in T_gi prediction above 37 °C. Contact T_forehead was shown to be a statistically suitable indicator of T_gi in non-HW conditions; however, an error of ~1 °C makes this physiologically redundant. The measurement of multiple sites may improve T_b prediction, though it is still physiologically unsuitable, especially at higher ambient temperatures.

The use of infrared thermography for the dynamic measurement of skin temperature of moving athletes during competition; methodological issues

Objective. To investigate the use of infrared thermography (IRT) for skin temperature measurement of moving athletes during competition and its sensitivity to factors that are traditionally standardised.Approach. Thermograms were collected for 18 female athletes during the 20 km racewalk at the 2019 World Athletics Championships, with a medium-wave, cooled indium antimonide medium wave infrared band (MWIR) and a long-wave, uncooled microbolometer longwave infrared band (LWIR) infrared camera.Main results. The MWIR provided greater clarity images of motion due to a shorter exposure and response time and produced a higher percentage of acceptable images. Analysing acceptable images only, the LWIR and WMIR produced good levels of agreement, with a bias of -0.1 ± 0.6 °C in mean skin temperature for the LWIR. As the surface area of an ROI was reduced, the measured temperature became less representative of the whole ROI. Compared to measuring the whole area ROI, a single central pixel produced a bias of 0.3 ± 0.3 °C (MWIR) and 0.1 ± 0.4 °C (LWIR) whilst using the maximum and minimum temperature pixels resulted in deviations of 1.3 ± 0.4 °C and -1.1 ± 0.3 °C (MWIR) and 1.2 ± 0.3 °C and -1.3 ± 0.4 °C (LWIR). The sensitivity to air and reflected temperatures was lower for the LWIR camera, due to the higher emissivity of skin in its wavelength.Significance. IRT provides an appropriate tool for the measurement of skin temperature during real-world competition and critically during athlete motion. The cheaper LWIR camera provides a feasible alternative to the MWIR in low rate of motion scenarios, with comparable precision and sensitivity to analysis. However, the LWIR is limited when higher speeds prevent the accurate measurement and ability to capture motion.

A Thermal Skin Model for Comparing Contact Skin Temperature Sensors and Assessing Measurement Errors

To improve the measurement and subsequent use of human skin temperature (Tsk) data, there is a need for practical methods to compare Tsk sensors and to quantify and better understand measurement error. We sought to develop, evaluate, and utilize a skin model with skin-like thermal properties as a tool for benchtop Tsk sensor comparisons and assessments of local temperature disturbance and sensor bias over a range of surface temperatures. Inter-sensor comparisons performed on the model were compared to measurements performed in vivo, where 14 adult males completed an experimental session involving rest and cycling exercise. Three types of Tsk sensors (two of them commercially available and one custom made) were investigated. Skin-model-derived inter-sensor differences were similar (within ±0.4 °C) to the human trial when comparing the two commercial Tsk sensors, but not for the custom Tsk sensor. Using the skin model, all surface Tsk sensors caused a local temperature disturbance with the magnitude and direction dependent upon the sensor and attachment and linearly related to the surface-to-environment temperature gradient. Likewise, surface Tsk sensors also showed bias from both the underlying disturbed surface temperature and that same surface in its otherwise undisturbed state. This work supports the development and use of increasingly realistic benchtop skin models for practical Tsk sensor comparisons and for identifying potential measurement errors, both of which are important for future Tsk sensor design, characterization, correction, and end use.

Non-contact infrared assessment of human body temperature: The journal Temperature toolbox

The assessment of human internal/core temperature (T core) is relevant in many scientific disciplines, but also for public health authorities when attempting to identify individuals with fever. Direct assessment of T core is often invasive, impractical on a large scale, and typically requires close contact between the observer and the target subject. Non-contact infrared thermometry (NCIT) represents a practical solution in which T core can potentially be assessed from a safe distance and in mass screening scenarios, by measuring skin temperature at specific anatomical locations. However, the COVID-19 pandemic has clearly demonstrated that these devices are not being used correctly, despite expert guided specifications available in International Standard Organization (ISO) documents. In this review, we provide an overview of the most pertinent factors that should be considered by users of NCIT. This includes the most pertinent methodological and physiological factors, as well as an overview on the ability of NCIT to track human T core. For practical use, we provide a checklist based on relevant ISO standards which are simple to follow and should be consulted prior to using NCIT for assessment of human T core. Our intention is for users of NCIT to adopt this checklist, which may improve the performance of NCIT for its ability to track T core.

Influence of infrared camera model and evaluator reproducibility in the assessment of skin temperature responses to physical exercise

Infrared thermography (IRT) has been gaining in popularity in clinical and scientific research due to the increasing availability of affordable infrared cameras. This study aims to determine the similarity of measurement performance between three models of IRT camera during assessment of skin temperature before and after physical exercise. Three models of FLIR thermographic cameras (E60bx, Flir-One Pro LT, and C2) were tested. Thermal images were taken of the foot sole, anterior leg, and anterior thigh from 12 well-trained men, before and after a 30-min run on a treadmill. Image files were blinded and processed by three evaluators to extract the mean, maximum, and standard deviation of skin temperature of the region of interest. Time for data processing and rate of perceived effort was also recorded. Data processing was slower on the E60bx (CI95% E60 vs C2 [0.2, 2.6 min], p = 0.02 and ES = 0.6); vs. Flir-One [0.0, 3.4 min], p = 0.03 and ES = 0.6) and was associated with lower effort perception (E60 3.0 ± 0.1 vs. Flir-One 5.6 ± 0.2 vs C2 7.0 ± 0.2 points; p < 0.001 and ES > 0.8). The C2 and Flir-One cameras underestimated the temperature compared with the E60. In general, measuring mean temperature provided higher camera and examiner intra-class correlations than maximum and standard deviation, especially before exercise. Moreover, post exercise mean skin temperatures provided the most consistent values across cameras and evaluators. We recommend the use of mean temperature and caution when using more than one camera model in a study.