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Home  >  Medical Research Archives  >  Issue 149  > Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach
Published in the Medical Research Archives
Jan 2024 Issue

Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach

Published on Jan 30, 2024

DOI 

Abstract

 

Respiration is vital for human function. Inhaling specific gases can have specific physiological and cognitive impacts. Using a suite of sensors, we can collect detailed information on a range of both physiological and environmental factors. This study builds on previous research exploring how particulate matter affects physiological and cognitive responses, now expanded to include CO2. We tracked the biometric variables of a cyclist, analyzing 329 specific variables. Simultaneously, an electric vehicle following the cyclist measured CO2 and other environmental factors. After data collection, we used machine learning models to decipher the interactions between the human body and its surroundings. We found that biometric data alone could be used to accurately estimate the amount of CO2 inhaled, achieving a good level of precision (r2=0.98) when comparing the estimated CO2 based on biometrics and the actual observed CO2 levels. In addition, we developed a ranking system to identify the biometric variables that most significantly predict environmental CO2 inhalation.

Author info

David Lary, Shisir Ruwali, Bharana Fernando, Shawhin Talebi, Lakitha Wijeratne, John Waczak, Vinu Sooriyaarachchi, Prabuddha Hathurusinghe, John Sadler, Tatiana Lary, Matthew Lary, Adam Aker

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