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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 ( ) data, there is a need for practical methods to compare 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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-07, Vol.21 (14), p.4906
Main Authors: MacRae, Braid A, Spengler, Christina M, Psikuta, Agnes, Rossi, René M, Annaheim, Simon
Format: Article
Language:English
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Summary:To improve the measurement and subsequent use of human skin temperature ( ) data, there is a need for practical methods to compare 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 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 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 sensors, but not for the custom sensor. Using the skin model, all surface 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 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 sensor comparisons and for identifying potential measurement errors, both of which are important for future sensor design, characterization, correction, and end use.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21144906