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

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

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.

Keywords: Machine learning, biometric, particulate matter, cognitive, CO2

Article Details

How to Cite
RUWALI, Shisir et al. Gauging Ambient Environmental Carbon Dioxide Concentration Solely Using Biometric Observations: A Machine Learning Approach. Medical Research Archives, [S.l.], v. 12, n. 1, jan. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/4890>. Date accessed: 03 mar. 2024. doi: https://doi.org/10.18103/mra.v12i1.4890.
Section
Research Articles

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