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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andersson, A., Wiréhn, A.B., Ölvander, C., Ekman, D.S., Bendtsen, P.: Alcohol use among university students in Sweden measured by an electronic screening instrument. BMC Public Health 9, 229 (2009)
Assaad, J.M., Pihl, R.O., Séguin, J.R., Nagin, D.S., Vitaro, F., Tremblay, R.E.: Intoxicated behavioral disinhibition and the heart rate response to alcohol. Exp. Clin. Psychopharmacol. 14(3), 377 (2006)
Baldwin, J.: Wearable blood alcohol measuring device, US Patent App. 14/298,847 (2014). http://www.google.com/patents/US20140365142
Chambers, M., Liu, M., Moore, C.: Drunk driving by the numbers
Dekker, D.O.A.: What are the effects of alcohol on the brain? July 1999. http://www.scientificamerican.com/article/what-are-the-effects-of-a/. Accessed 16 August 2015
Guiry, J.J., van de Ven, P., Nelson, J.: Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices. Sensors 14(3), 5687–5701 (2014)
Koukiou, G., Anastassopoulos, V.: Neural networks for identifying drunk persons using thermal infrared imagery. Forensic Sci. Int. 252, 69–76 (2015). http://www.sciencedirect.com/science/article/pii/S0379073815001681
Landro, L.: Why connected medicine is becoming vital to health care, June 2015. http://www.wsj.com/articles/why-connected-medicine-is-becoming-vital-to-health-care-1435243250
Hacking health: How consumers use smartphones and wearable tech to track their health, April 2014. http://www.nielsen.com/us/en/insights/news/2014/hacking-health-how-consumers-use-smartphones-and-wearable-tech-to-track-their-health.html
Posey, D., Mozayani, A.: The estimation of blood alcohol concentration. Forensic Sci. Med. Pathol. 3(1), 33–39 (2007)
Schoenborn, C.A., Adams, P.F., Peregoy, J.A.: Health behaviors of adults: United states, 2008–2010. Vital Health Stat. 257, 14–29 (2013). Data from the National Health Survey
Sharma, V., Mankodiya, K., De La Torre, F., Zhang, A., Ryan, A., Ton, T.G.N., Gandhi, R., Jain, S.: SPARK: personalized parkinson disease interventions through synergy between a smartphone and a smartwatch. In: Marcus, A. (ed.) DUXU 2014, Part III. LNCS, vol. 8519, pp. 103–114. Springer, Heidelberg (2014)
Tacconi, C., Mellone, S., Chiari, L.: Smartphone-based applications for investigating falls and mobility. In: 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), pp. 258–261, May 2011
Ward, L.: How to cut student drinking. Wall Street J., p. R5 (2015)
Webster, G.D., Gabler, H.C.: Feasibility of transdermal ethanol sensing for the detection of intoxicated drivers. Associating Adv. Automot. Med. 51, 449–464 (2007)
Wikipedia, : Blood alcohol content – wikipedia, the free encyclopedia (2015). https://en.wikipedia.org/w/index.php?title=Blood_alcohol_content&oldid=674051485. Accessed 13 July 2015
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-29175-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29174-1
Online ISBN: 978-3-319-29175-8
eBook Packages: Computer ScienceComputer Science (R0)