research-article

Public Access

Drunk User Interfaces: Determining Blood Alcohol Level through Everyday Smartphone Tasks

Authors:

Alex Mariakakis,

Shwetak N. Patel,

Jacob O. WobbrockAuthors Info & Claims

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

Paper No.: 234, Pages 1 - 13

https://doi.org/10.1145/3173574.3173808

Published: 21 April 2018 Publication History

Abstract

Breathalyzers, the standard quantitative method for assessing inebriation, are primarily owned by law enforcement and used only after a potentially inebriated individual is caught driving. However, not everyone has access to such specialized hardware. We present drunk user interfaces: smartphone user interfaces that measure how alcohol affects a person's motor coordination and cognition using performance metrics and sensor data. We examine five drunk user interfaces and combine them to form the "DUI app". DUI uses machine learning models trained on human performance metrics and sensor data to estimate a person's blood alcohol level (BAL). We evaluated DUI on 14 individuals in a week-long longitudinal study wherein each participant used DUI at various BALs. We found that with a global model that accounts for user-specific learning, DUI can estimate a person's BAL with an absolute mean error of 0.005% ± 0.007% and a Pearson's correlation coefficient of 0.96 with breathalyzer measurements.

Supplementary Material

MP4 File (pn2428.mp4)

Download
239.46 MB

References

[1]

Johnny Accot and Shumin Zhai. 1997. Beyond https://fruitninja.com/ Fitts' law: Models for trajectory-based HCI tasks. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '97), 295--302.

Digital Library

[2]

Zachary Arnold, Danielle LaRose, and Emmanuel Agu. 2015. Smartphone Inference of Alcohol Consumption Levels from Gait. In Proceedings 2015 IEEE International Conference on Healthcare Informatics, ICHI 2015, 417--426.

Digital Library

[3]

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. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 2: 1--36.

Digital Library

[4]

Robert F Borkenstein and H W Smith. 1961. The breathalyzer and its application. Medicine, Science and the Law 1: 13. Retrieved March 23, 2017 from http://msl.sagepub.com/content/2/1/13.short

[5]

Clay Bradley and Gage A Brasher. 2013. Nystagmus evaluation system. Retrieved July 13, 2017 from https://www.google.com/patents/US9042615

[6]

Robert L Breckenridge and R Scott Berger. 1990. Locus of Control and Perceived Alcohol Ingestion in Performance of a Fine Motor Skill. Psychological Reports 66, 1: 179--185.

[7]

M Brenner and J R Cash. 1991. Speech analysis as an index of alcohol intoxication--the Exxon Valdez accident. Aviation, space, and environmental medicine 62, 9 Pt 1: 893--8. Retrieved August 19, 2017 from http://www.ncbi.nlm.nih.gov/ /1930083

[8]

Stuart K Card, Thomas P Moran, and Allen Newell. 1983. The Psychology of Human-Computer Interaction. L. Erlbaum Associates.

Digital Library

[9]

Federal Trade Commission. 2017. FTC v. Breathometer, Inc. 1--20. Retrieved from www.ftc.gov/system/files/documents/cases/170123 breathometer_dkt._4--1_-_stipulated_order.pdf

[10]

Drew Dawson and Kathryn Reid. 1997. Fatigue, alcohol and performance impairment. Nature 388, 6639: 235--235.

[11]

David F Dinges and John W Powell. 1985. Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Behavior Research Methods, Instruments, & Computers 17, 6: 652--655.

[12]

Edward Elton, D Dumolo, and Colette A Nicolle. 2010. Have I just pressed something? The effects of everyday cold temperatures on dexterity. IN: Designing Inclusive Interactions: Inclusive Interactions between. In 5th Cambridge Workshop on Universal Access and Assistive Technology, 22-- 25. Retrieved July 14, 2017 from http://www.academia.edu/download/42191250/PU B_20571_20The_20effects_20of_20everyday_20co ld_20temperatures_20on_20dexterity.pdf

[13]

Abigail Evans and Jacob Wobbrock. 2012. Taming Wild Behavior: The Input Observer for Obtaining Text Entry and Mouse Pointing Measures from Everyday Computer Use. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1947--1956.

Digital Library

[14]

Federal Bureau of Investigation. 2014. Crime in the United States - 2014. Retrieved July 4, 2017 from https://ucr.fbi.gov/crime-in-the-u.s/2014/crime-inthe-u.s.-2014/tables/table-29

[15]

T Flash and N Hogan. 1985. The coordination of arm movements: an experimentally confirmed mathematical model. The Journal of neuroscience 5, 7: 1688--1703. https://doi.org/4020415

[16]

Mayank Goel, Leah Findlater, and Jacob Wobbrock. 2012. WalkType: Using Accelerometer Data to Accomodate Situational Impairments in Mobile Touch Screen Text Entry. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems - CHI '12, 2687-- 2696.

Digital Library

[17]

S A Grant, K Millar, and G N Kenny. 2000. Blood alcohol concentration and psychomotor effects. British journal of anaesthesia 85, 3: 401--406.

[18]

Teng Han, Xiang Xiao, Lanfei Shi, John Canny, and Jingtao Wang. 2015. Balancing Accuracy and Fun: Designing Camera Based Mobile Games for Implicit Heart Rate Monitoring. In Proceedings of the ACM CHI'15 Conference on Human Factors in Computing Systems, 847--856.

Digital Library

[19]

Rolla N Harger. 1948. Chemical Tests for Intoxication as an Aid to Traffic and Industrial Safety. Industrial Medicine and Surgery 17, 2: 52-- 56. Retrieved July 4, 2017 from https://www.cabdirect.org/cabdirect/abstract/19482

[20]

Shahin Hashtroudi, Elizabeth S Parker, Lynn E DeLisi, Richard J Wyatt, and Sharon A Mutter. 1984. Intact retention in acute alcohol amnesia. Journal of Experimental Psychology: Learning, Memory, and Cognition 10, 1: 156--163. Retrieved July 6, 2017 from http://psycnet.apa.org/journals/xlm/10/1/156/

[21]

I Hindmarch, J S Kerr, and N Sherwood. 1991. The effects of alcohol and other drugs on psychomotor performance and cognitive function. Alcohol and alcoholism 26, 1: 71--79. Retrieved July 14, 2017 from http://www.ncbi.nlm.nih.gov/ /1854375

[22]

Nabil Hossain, Tianran Hu, Roghayeh Feizi, Ann Marie White, Jiebo Luo, and Henry Kautz. 2016. Inferring Fine-grained Details on User Activities and Home Location from Social Media: Detecting Drinking-While-Tweeting Patterns in Communities. Retrieved July 11, 2017 from http://arxiv.org/abs/1603.03181

[23]

Amy Jewett, Ruth A Shults, Tanima Banerjee, and Gwen Bergen. 2015. Alcohol-impaired driving among adults-United States, 2012. Morbidity and Mortality Weekly Report (MMWR) 64, 30: 814-- 817. Retrieved July 4, 2017 from https://www.cdc.gov/Mmwr/preview/mmwrhtml/m m6430a2.htm

[24]

Peter Johnson. 1998. Usability and Mobility; Interactions on the move. In First workshop on human computer interaction with mobile devices.

[25]

Youngkee Jung, Jinhee Kim, Olumide Awofeso, Huisung Kim, Fred Regnier, and Euiwon Bae. 2015. Smartphone-based colorimetric analysis for detection of saliva alcohol concentration. Applied Optics 54, 31: 9183--9.

[26]

Hsin-Liu (Cindy) Kao, Bo-Jhang Ho, Allan C Lin, and Hao-Hua Chu. 2012. Phone-based gait analysis to detect alcohol usage. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing UbiComp '12, 661--662. Retrieved July 11, 2017 from http://dl.acm.org/citation.cfm?id=2370354

Digital Library

[27]

Matthew Kay, Kyle Rector, Sunny Consolvo, Ben Greenstein, Jacob Wobbrock, Nathaniel Watson, and Julie Kientz. 2013. PVT-Touch: Adapting a Reaction Time Test for Touchscreen Devices. In Proceedings of the ICTs for improving Patients Rehabilitation Research Techniques, 248--251.

Digital Library

[28]

F. Klingholz, R. Penning, and E. Liebhardt. 1988. Recognition of low-level alcohol intoxication from speech signal. The Journal of the Acoustical Society of America 84, 3: 929--935.

[29]

LF Kozachenko and NN Leonenko. 1987. Sample estimate of the entropy of a random vector. Problemy Peredachi Informatsii 23, 2: 9--16. Retrieved March 28, 2017 from http://www.mathnet.ru/eng/ppi797

[30]

Per-Ola Kristensson and Shumin Zhai. 2004. SHARK2 : A Large Vocabulary Shorthand Writing System for Pen-Based Computers. Proceedings of the 17th annual ACM symposium on User interface software and technology - UIST '04 6, 2: 43--52.

Digital Library

[31]

RJ Lawrence. 1988. The log-normal as event--time distribution. Dekker, New York, New York, USA.

[32]

I Scott MacKenzie, Tatu Kauppinen, and Miika Silfverberg. 2001. Accuracy measures for evaluating computer pointing devices. In Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '01, 9--16.

Digital Library

[33]

I Scott MacKenzie and R William Soukoreff. 2003. Phrase sets for evaluating text entry techniques. In CHI '03 extended abstracts on Human factors in computing systems - CHI '03, 754--755.

Digital Library

[34]

Alexander Mariakakis, Mayank Goel, Md Tanvir Islam Aumi, Shwetak N Patel, and Jacob O Wobbrock. 2015. SwitchBack: Using Focus and Saccade Tracking to Guide Users' Attention for Mobile Task Resumption. In Proceedings of the ACM CHI'15 Conference on Human Factors in Computing Systems, 2953--2962.

Digital Library

[35]

National Highway Traffic Safety Association. 2015. Traffic Safety Facts. 2014 Data.

[36]

Allen F Newell. 1995. Extra-ordinary humancomputer interaction. Cambridge University Press. Retrieved September 12, 2017 from http://dl.acm.org/citation.cfm?id=215600

[37]

Mary E Nicholson, MinQi Wang, Collins O Airhihenbuwa, Beverly S Mahoney, and Dolores W Maney. 1992. Predicting alcohol impairment: Perceived intoxication versus BAC. Alcoholism: Clinical and Experimental Research 16, 4: 747-- 750. Retrieved July 6, 2017 from

[38]

Suchismita Ray and Marsha E Bates. 2006. Acute alcohol effects on repetition priming and word recognition memory with equivalent memory cues. Brain and Cognition 60, 2: 118--127.

[39]

J M Ryan and L G Howes. 2002. Relations between alcohol consumption, heart rate, and heart rate variability in men. Heart (British Cardiac Society) 88, 6: 641--2. Retrieved July 18, 2017 from http://www.ncbi.nlm.nih.gov/ /12433903

[40]

Andrew Sears and Julie A. Jacko. 2008. Physical disabilities and computing technologies: An analysis of impairments. Lawrence Erlbaum Associates, Hillsdale, New Jersey.

[41]

Andrew Sears, Min Lin, Julie Jacko, and Yan Xiao. 2003. When computers fade: Pervasive computing and situationally-induced impairments and disabilities. HCI International 2, 3: 1298--1302.

[42]

R William Soukoreff and I Scott MacKenzie. 2003. Metrics for text entry research: an evaluation of MSD and KSPC, and a new unified error metric. In Proceedings of the conference on Human factors in computing systems - CHI '03, 113--120.

Digital Library

[43]

H Tianwu, Yoshinori Watanabe, M Asai, K Shimizu, S Takada, and K Mizukoshi. 1995. Effects of alcohol ingestion on vestibular function in postural control. Acta Oto-Laryngologica 115, S519: 127--131.

[44]

Tin Kam Ho. 1995. Random decision forests. Proceedings of 3rd International Conference on Document Analysis and Recognition 1: 278--282.

Digital Library

[45]

Shari Trewin. 2002. An invisible keyguard. In Proceedings of the fifth international ACM conference on Assistive technologies, 143--149.

Digital Library

[46]

Gregg C Vanderheiden. 1997. Anywhere, anytime (+anyone) access to the next-generation WWW. Computer Networks and ISDN Systems 29, 8: 1439--1446.

Digital Library

[47]

Emma R Weaver, Danielle R Horyniak, Rebecca Jenkinson, Paul Dietze, and Megan Sc Lim. 2013. "Let's get Wasted!" and Other Apps: Characteristics, Acceptability, and Use of AlcoholRelated Smartphone Applications. JMIR mHealth and uHealth 1, 1: e9.

[48]

JM Weiler, JR Bloomfield, GG Woodworth, AR Grant, TA Layton, TL Brown, DR McKenzie, TW Baker, and GS Watson. 2000. Effect of fexofenadine, diphenhydramine, and alcohol on driving performance. Annals of Internal Medicine 132, 5: 354--363. Retrieved August 21, 2017 from http://annals.org/aim/article/713291/effects-fexofenadine-diphenhydramine-alcohol-driving-performance-randomized-placebo-controlled-trial

[49]

A M Williamson. 2000. Moderate sleep deprivation produces impairments in cognitive and motor performance equivalent to legally prescribed levels of alcohol intoxication. Occupational and Environmental Medicine 57, 10: 649--655.

[50]

Jacob O Wobbrock. 2006. The Future of Mobile Device Research in HCI. In Proc. CHI '06 Workshops, 131-- 134. Retrieved January 6, 2018 from http://faculty.washington.edu/wobbrock/pubs/chi-06.05.pdf

[51]

Jacob O Wobbrock, Krzysztof Z Gajos, Shaun K Kane, and Gregg C Vanderheiden. Ability-Based Design. Communications of the ACM.

[52]

Jacob O Wobbrock and Brad A Myers. 2006. Analyzing the input stream for character- level errors in unconstrained text entry evaluations. ACM Transactions on Computer-Human Interaction 13, 4: 458--489.

Digital Library

Cited By

Lee HKim ABae SLee U(2024)S-ADL: Exploring Smartphone-based Activities of Daily Living to Detect Blood Alcohol Concentration in a Controlled EnvironmentProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642832(1-25)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642832
Liu XLi JKim DChen XDu R(2024)Human I/O: Towards a Unified Approach to Detecting Situational ImpairmentsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642065(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642065
Bae SSuffoletto BZhang TChung TOzolcer MIslam MDey A(2023)Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation StudyJMIR Formative Research10.2196/398627(e39862)Online publication date: 4-May-2023
https://doi.org/10.2196/39862
Show More Cited By

Index Terms

Drunk User Interfaces: Determining Blood Alcohol Level through Everyday Smartphone Tasks
1. Applied computing
  1. Life and medical sciences
2. Social and professional topics
  1. Computing / technology policy

Recommendations

Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Excessive alcohol consumption causes disability and death. Digital interventions are promising means to promote behavioral change and thus prevent alcohol-related harm, especially in critical moments such as driving. This requires real-time information ...
Understanding Parents’ Concerns with Smart Device Usage in the Home
HCI for Cybersecurity, Privacy and Trust
Abstract
Several studies have been conducted to determine parenting strategies in the age of digital technology. However, we are not aware of any qualitative research regarding parents’ safety and privacy concerns about their children’s use of smart ...
The reciprocal associations between sharing alcohol references on social networking sites and binge drinking

Accumulating evidence indicates that social media use and the consumption of alcohol are related. The present longitudinal study (N=1006) adds to this line of research by examining the reciprocity of sharing alcohol references on social media and binge ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '18: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

April 2018

8489 pages

ISBN:9781450356206

DOI:10.1145/3173574

General Chairs:
Regan Mandryk
University of Saskatchewan, Canada
,
Mark Hancock
University of Waterloo, Canada
,
Program Chairs:
Mark Perry
Brunel University London, UK
,
Anna Cox
University College London, UK

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

NSF
Mani Charitable Foundation.
Google Faculty Award

Conference

CHI '18

Sponsor:

SIGCHI

CHI '18: CHI Conference on Human Factors in Computing Systems

April 21 - 26, 2018

Montreal QC, Canada

Acceptance Rates

CHI '18 Paper Acceptance Rate 666 of 2,590 submissions, 26%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

25
Total Citations
View Citations
1,581
Total Downloads

Downloads (Last 12 months)359
Downloads (Last 6 weeks)56

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lee HKim ABae SLee U(2024)S-ADL: Exploring Smartphone-based Activities of Daily Living to Detect Blood Alcohol Concentration in a Controlled EnvironmentProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642832(1-25)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642832
Liu XLi JKim DChen XDu R(2024)Human I/O: Towards a Unified Approach to Detecting Situational ImpairmentsProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642065(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642065
Bae SSuffoletto BZhang TChung TOzolcer MIslam MDey A(2023)Leveraging Mobile Phone Sensors, Machine Learning, and Explainable Artificial Intelligence to Predict Imminent Same-Day Binge-drinking Events to Support Just-in-time Adaptive Interventions: Algorithm Development and Validation StudyJMIR Formative Research10.2196/398627(e39862)Online publication date: 4-May-2023
https://doi.org/10.2196/39862
Kong HSingh V(2023)Toward Cognition-Aware Digital Phenotyping for Substance Use DisorderAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3612877(638-641)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1145/3594739.3612877
Roe DMarathe M(2023)Examining Race in Healthcare-Focused HCI ResearchCompanion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3584931.3607021(22-26)Online publication date: 14-Oct-2023
https://dl.acm.org/doi/10.1145/3584931.3607021
Koch KMaritsch MVan Weenen EFeuerriegel SPfäffli MFleisch EWeinmann WWortmann F(2023)Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk drivingProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580975(1-32)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580975
Ning ECladek ARoss MKabir SBarve AKennelly EHussain FDuffecy JLangenecker SNguyen TTulabandhula TZulueta JAjilore ODemos ALeow A(2023)Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain HealthProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3580906(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3580906
Nassi BShams JRokach LElovici Y(2022)Virtual Breathalyzer: Towards the Detection of Intoxication Using Motion Sensors of Commercial Wearable DevicesSensors10.3390/s2209358022:9(3580)Online publication date: 8-May-2022
https://doi.org/10.3390/s22093580
Rodrigues ANicolau HSantos ABranco DRainey JVerweij DSmeddinck JMontague KGuerreiro T(2022)Investigating the Tradeoffs of Everyday Text-Entry Collection MethodsProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3501908(1-15)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3501908
Chen YXue MZhang JOu RZhang QKuang P(2022) DetectDUI : An In-Car Detection System for Drink Driving and BACs IEEE/ACM Transactions on Networking10.1109/TNET.2021.312595030:2(896-910)Online publication date: Apr-2022
https://doi.org/10.1109/TNET.2021.3125950
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents