Abstract
Inappropriate alcohol drinking may cause health and social problems. Although controlling the intake of alcohol is effective to solve the problem, it is laborious to track consumption manually. A system that automatically records the amount of alcohol consumption has a potential to improve behavior in drinking activities. Existing devices and systems support drinking activity detection and liquid intake estimation, but our target scenario requires the capability of determining the alcohol concentration of a beverage. We present Al-light, a smart ice cube to detect the alcohol concentration level of a beverage using an optical method. Al-light is the size of 31.9 x 38.6 x 52.6 mm and users can simply put it into a beverage for estimation. It embeds near infrared (1450 nm) and visible LEDs, and measures the magnitude of light absorption. Our device design integrates prior technology in a patent which exploits different light absorption properties between water and ethanol to determine alcohol concentration. Through our revisitation studies, we found that light at the wavelength of 1450 nm has strong distinguishability even with different types of commercially-available beverages. Our quantitative examinations on alcohol concentration estimation revealed that Al-light was able to achieve the estimation accuracy of approximately 2 % v/v with 13 commercially-available beverages. Although our current approach needs a regressor to be trained for a particular ambient light condition or the sensor to be calibrated using measurements with water, it does not require beverage-dependent models unlike prior work. We then discuss four applications our current prototype supports and future research directions.
Supplemental Material
Available for Download
Supplemental movie, appendix, image and software files for, Al-light: An Alcohol-Sensing Smart Ice Cube
- Roche Ann, Bywood Petra, Freeman Toby, Pidd Ken, Borlagdan Joseph, and Allan Trifonoff. 2009. The Social Context of Alcohol Use in Australia. National Centre for Education and Training on Addiction (NCETA), Adelaide, Australia.Google Scholar
- Sangwon Bae, Denzil Ferreira, Brian Suffoletto, Juan C. Puyana, Ryan Kurtz, Tammy Chung, and Anind K. Dey. 2017. Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 2, Article 5 (June 2017), 36 pages. Google ScholarDigital Library
- Roman Benes. 2012. Method and device for determining an alcohol content of liquids, US Patent 8,106,361. (Jan. 2012).Google Scholar
- Quansheng Chen, Jiewen Zhao, C.H. Fang, and Dongmei Wang. 2007. Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM). Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 66, 3 (2007), 568--574.Google ScholarCross Ref
- Dhairya Dand. 2013. Cheers: Alcohol-aware Strobing Ice Cubes. In Proceedings of the CHI '13 Extended Abstracts on Human Factors in Computing Systems (CHI EA '13). ACM, New York, NY, USA, 2795--2796. Google ScholarDigital Library
- Kiyah J. Duffey and Barry M. Popkin. 2007. Shifts in patterns and consumption of beverages between 1965 and 2002. Obesity 15, 11 (2007), 2739--2747.Google ScholarCross Ref
- Mingming Fan, Khai N Truong, and Abhishek Ranjan. 2016. Exploring the Use of Capacitive Sensing to Externally Measure Liquid in Fluid Containers. Technical Report. Knowledge Media Design Institute, University of Toronto.Google Scholar
- Denzil Ferreira, Vassilis Kostakos, and Anind K. Dey. 2015. AWARE: Mobile Context Instrumentation Framework. Frontiers in ICT 2 (2015), 6.Google ScholarCross Ref
- Máximo Gallignani, Salvador Garrigues, and Miguel de la Guardia. 1993. Direct determination of ethanol in all types of alcoholic beverages by near-infrared derivative spectrometry. Analyst 118 (1993), 1167--1173. Issue 9.Google ScholarCross Ref
- Mario A. Gutierrez, Michelle L. Fast, Anne H. Ngu, and Byron J. Gao. 2016. Real-Time Prediction of Blood Alcohol Content Using Smartwatch Sensor Data. In Revised Selected Papers of the International Conference on Smart Health - Volume 9545 (ICSH 2015). Springer-Verlag New York, Inc., New York, NY, USA, 175--186. Google ScholarDigital Library
- Pei-Yi Hsu, Ya-Han Lee, Chuang-Wen You, Yaliang Chuang, Ming-Chyi Huang, and Hao-Chuan Wang. 2017. Learning How Clinicians Use Self-logged Behavior Data when Managing Patients with Alcohol Use Problems in a Clinical Setting. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (UbiComp '17). ACM, New York, NY, USA, 73--76. Google ScholarDigital Library
- Pei-Yi Hsu, Ya-Fang Lin, Jian-Lun Huang, Chih-Chun Chang, Shih-Yao Lin, Ya-Han Lee, Chuang-Wen You, Yaliang Chuang, Ming-Chyi Huang, Hsin-Tung Tseng, and Hao-Chuan Wang. 2017. A Mobile Support System to Assist DUI Offenders on Probation in Reducing DUI Relapse. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (UbiComp '17). ACM, New York, NY, USA, 77--80. Google ScholarDigital Library
- AOAC International. {n. d.}. AOAC Official Method 957.03 Alcohol by Volume in Distilled Liquors. http://www.eoma.aoac.org/methods/info.asp?ID=233. ({n. d.}). (Accessed on 2018/02/08).Google Scholar
- Pascal Lessel, Maximilian Altmeyer, Frederic Kerber, Michael Barz, Cornelius Leidinger, and Antonio Krüger. 2016. WaterCoaster: A Device to Encourage People in a Playful Fashion to Reach Their Daily Water Intake Level. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 1813--1820. Google ScholarDigital Library
- J. Lester, D. Tan, S. Patel, and A. J. B. Brush. 2010. Automatic classification of daily fluid intake. In Proceedings of the 4th International Conference on Pervasive Computing Technologies for Healthcare. 1--8.Google ScholarCross Ref
- Fei Liu, Xujun Ye, Yong He, and Li Wang. 2009. Application of visible/near infrared spectroscopy and chemometric calibrations for variety discrimination of instant milk teas. Journal of Food Engineering 93, 2 (2009), 127--133.Google ScholarCross Ref
- James R. McKay, Teresa R. Franklin, Nicholas Patapis, and Kevin G. Lynch. 2006. Conceptual, methodological, and analytical issues in the study of relapse. Clinical Psychology Review 26, 2 (2006), 109--127.Google ScholarCross Ref
- Mark Muraven, R Lorraine Collins, Saul Shiffman, and Jean A Paty. 2005. Daily fluctuations in self-control demands and alcohol intake. Psychology of addictive behaviors: journal of the Society of Psychologists in Addictive Behaviors 19, 2 (June 2005), 140--147.Google Scholar
- Jeremy Northcote and Michael Livingston. 2011. Accuracy of Self-Reported Drinking: Observational Verification of 'Last Occasion' Drink Estimates of Young Adults. Alcohol and Alcoholism 46, 6 (2011), 709--713.Google ScholarCross Ref
- Celio Pasquini. 2003. Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications. Journal of the Brazilian Chemical Society 14 (04 2003), 198--219. http://www.scielo.br/scielo.php?script=sci_arttext8pid=S0103-505320030002000068nrm=isoGoogle Scholar
- M.J.C. Pontes, S.R.B. Santos, M.C.U. Araújo, L.F. Almeida, R.A.C. Lima, E.N. Gaião, and U.T.C.P. Souto. 2006. Classification of distilled alcoholic beverages and verification of adulteration by near infrared spectrometry. Food Research International 39, 2 (2006), 182--189.Google ScholarCross Ref
- Benjamin Poppinga, Jutta Fortmann, Heiko Müller, Wilko Heuten, and Susanne Boll. 2014. IllumiMug: Revealing Imperceptible Characteristics of Drinks. In Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational (NordiCHI '14). ACM, New York, NY, USA, 923--926. Google ScholarDigital Library
- Tauhidur Rahman, Alexander T. Adams, Perry Schein, Aadhar Jain, David Erickson, and Tanzeem Choudhury. 2016. Nutrilyzer: A Mobile System for Characterizing Liquid Food with Photoacoustic Effect. In Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems CD-ROM (SenSys '16). ACM, New York, NY, USA, 123--136. Google ScholarDigital Library
- Jeffrey S. Simons, Thomas A. Wills, Noah N. Emery, and Russell M. Marks. 2015. Quantifying alcohol consumption: Self-report, transdermal assessment, and prediction of dependence symptoms. Addictive Behaviors 50 (2015), 205--212.Google ScholarCross Ref
- The International Alliance for Responsible Drinking (IARD). {n. d.}. Drinking Guidelines: General Population. available from <http://www.iard.org/policy-tables/drinking-guidelines-general-population/>. ({n. d.}).Google Scholar
- Chuang-wen You, Kuo-Cheng Wang, Ming-Chyi Huang, Yen-Chang Chen, Cheng-Lin Lin, Po-Shiun Ho, Hao-Chuan Wang, Polly Huang, and Hao-Hua Chu. 2015. SoberDiary: A Phone-based Support System for Assisting Recovery from Alcohol Dependence. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). ACM, New York, NY, USA, 3839--3848. Google ScholarDigital Library
Index Terms
- Al-light: An Alcohol-Sensing Smart Ice Cube
Recommendations
Probing Sucrose Contents in Everyday Drinks Using Miniaturized Near-Infrared Spectroscopy Scanners
Near-Infrared Spectroscopy (NIRS) is a non-invasive sensing technique which can be used to acquire information on an object's chemical composition. Although NIRS is conventionally used in dedicated laboratories, the recent introduction of miniaturized ...
Toward Novel Sensing Technology for Personal Healthcare
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable ComputersInappropriate alcohol drinking may cause health and social problems. Although controlling the intake of alcohol is effective to solve the problem, it is laborious to track consumption manually. A system that automatically records the amount of alcohol ...
Feasibility Analysis of Lower Limit of Quantification of NIR for Solvent in Different Hydrogen Bonds Environment Using Multivariate Calibrations
ICBEB '12: Proceedings of the 2012 International Conference on Biomedical Engineering and BiotechnologyNear infrared (NIR) transmission spectroscopy has been widely used for quantitative analysis in different solvents system. Lower limit of quantification (LLOQ) of NIR in solvent of different hydrogen bonding capability has not been reported. This paper ...
Comments