Towards Online Personalized-Monitoring of Human Thermal Sensation Using Machine Learning Approach

Today, we would like to share a new paper about online personalized-monitoring of human thermal sensation using machine learning approaches. It is authored by Ali Youssef, Ahmed Youssef Ali Amer, Nicolás Caballero and Jean Marie Arts, from the KU Leuven, and published in the MDPI open access publishing platform.

To improve thermal comfort of building occupants, adaptive models enabling personalized climate control is becoming increasingly important. The aim of the research was to develop a personalized adaptive classification model to predict individual’s thermal sensation based on non-intrusive and easily measured variables, using already available wearable sensors. One of those used sensors was greenTEG’s gSKIN BodyTemp patch. The developed model predicted an individual’s thermal sensation with an accuracy of 86%.

We would like to congratulate the authors for the published paper and the results of their research. For more information on our core body temperature sensors, please visit our webpage.

 

 

 

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