Abstract
This paper proposes an application that collects sensor data from a smartwatch in order to predict drunkenness in real-time, discreetly, and non-invasively via a machine learning approach. This system could prevent drunk driving or other dangers related to the consumption of alcohol by giving users a way to determine personal intoxication level without the use of intrusive breathalyzers or guess work. Using smartwatch data collected from several volunteers, we trained a machine learning model that may work with a smartphone application to predict the user’s intoxication level in real-time.
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Acknowledgement
We thank the National Science Foundation for funding the research under the Research Experiences for Undergraduates Program (CNS-1358939) at Texas State University to perform this piece of work and the infrastructure provided by a NSF-CRI 1305302 award.
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Gutierrez, M.A., Fast, M.L., Ngu, A.H., Gao, B.J. (2016). Real-Time Prediction of Blood Alcohol Content Using Smartwatch Sensor Data. In: Zheng, X., Zeng, D., Chen, H., Leischow, S. (eds) Smart Health. ICSH 2015. Lecture Notes in Computer Science(), vol 9545. Springer, Cham. https://doi.org/10.1007/978-3-319-29175-8_16
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DOI: https://doi.org/10.1007/978-3-319-29175-8_16
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