skip to main content
10.1145/3329189.3329212acmotherconferencesArticle/Chapter ViewAbstractPublication PagespervasivehealthConference Proceedingsconference-collections
research-article

Detection of a Smartphone User's Distraction Based on Typing and Touch Gestures

Published:20 May 2019Publication History

ABSTRACT

Distraction influences how we experience user interfaces and perform tasks as it increases our workload. It can be caused by internal or external factors. However, it is difficult to measure it in everyday life situations. Smartphones might solve this problem. To examine correlations between smartphone features and distraction, we conducted a user study with 20 participants. They had to (1) type text to describe a picture and (2) swipe and tap to select images from a gallery, either with or without distraction. Ground truth data (NASA-TLX, task completion time, eye pupil diameter) confirmed a successful distraction. Distraction went along with significant changes in touch and typing behavior. That is, participants tended to type slower (higher inter-key interval), made more typing errors (higher keystroke per character), and swiped slower (lower swiping speed). We conclude that a detection of distraction is possible, but only relative to a basic or previous user behavior that needs to be known.

References

  1. {n. d.}. Affectiva. http://www.affectiva.com/. Accessed: 2018-08-28.Google ScholarGoogle Scholar
  2. {n. d.}. distracted - Wiktionary. https://en.wiktionary.org/wiki/distracted. Accessed: 2018-04-25.Google ScholarGoogle Scholar
  3. {n. d.}. distraction - Wiktionary. https://en.wiktionary.org/wiki/distraction. Accessed: 2018-04-25.Google ScholarGoogle Scholar
  4. {n. d.}. HTC One (M8) -- User Manual. http://www.htc.com/us/smartphones/htc-one-m8/. Accessed: 2018-08-28.Google ScholarGoogle Scholar
  5. {n. d.}. Pupil Labs -- Pupil. https://pupil-labs.com/pupil/. Accessed: 2018-08-28.Google ScholarGoogle Scholar
  6. Michael J Albers. 2012. Human-Information Interaction and Technical Communication: Concepts and Frameworks: Concepts and Frameworks. IGI Global. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. D Alan Allport, Barbara Antonis, and Patricia Reynolds. 1972. On the division of attention: A disproof of the single channel hypothesis. The Quarterly journal of experimental psychology 24, 2 (1972), 225--235.Google ScholarGoogle Scholar
  8. Pavlo Antonenko, Fred Paas, Roland Grabner, and Tamara van Gog. 2010. Using electroencephalography to measure cognitive load. Educational Psychology Review 22, 4 (2010), 425--438.Google ScholarGoogle ScholarCross RefCross Ref
  9. Brian P Bailey, Joseph A Konstan, and John V Carlis. 2000. Measuring the effects of interruptions on task performance in the user interface. In Systems, Man, and Cybernetics, 2000 IEEE International Conference on, Vol. 2. IEEE, 757--762.Google ScholarGoogle Scholar
  10. Oswald Bratfisch et al. 1972. Perceived Item-Difficulty in Three Tests of Intellectual Performance Capacity. (1972).Google ScholarGoogle Scholar
  11. Ricardo Buettner. 2013. Cognitive workload of humans using artificial intelligence systems: towards objective measurement applying eye-tracking technology. In Annual Conference on Artificial Intelligence. Springer, 37--48.Google ScholarGoogle ScholarCross RefCross Ref
  12. Shiwei Cheng. 2011. The research framework of eye-tracking based mobile device usability evaluation. In Proceedings of the 1st international workshop on pervasive eye tracking & mobile eye-based interaction. ACM, 21--26. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. F Thomas Eggemeier. 1988. Properties of workload assessment techniques. Advances in psychology 52 (1988), 41--62.Google ScholarGoogle Scholar
  14. Anja Exler, Marcel Braith, Andrea Schankin, and Michael Beigl. 2016. Preliminary investigations about interruptibility of smartphone users at specific place types. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct. ACM, 1590--1595. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Anna Maria Feit, Daryl Weir, and Antti Oulasvirta. 2016. How We Type: Movement Strategies and Performance in Everyday Typing. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 4262--4273. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Joel E Fischer, Chris Greenhalgh, and Steve Benford. 2011. Investigating episodes of mobile phone activity as indicators of opportune moments to deliver notifications. In Proceedings of the 13th international conference on human computer interaction with mobile devices and services. ACM, 181--190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Yuan Gao, Nadia Bianchi-Berthouze, and Hongying Meng. 2012. What does touch tell us about emotions in touchscreen-based gameplay? ACM Transactions on Computer-Human Interaction (TOCHI) 19, 4 (2012), 31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Tony Gillie and Donald Broadbent. 1989. What makes interruptions disruptive? A study of length, similarity, and complexity. Psychological research 50, 4 (1989), 243--250.Google ScholarGoogle Scholar
  19. Tiffany Grunwald and Charisse Corsbie-Massay. 2006. Guidelines for cognitively efficient multimedia learning tools: educational strategies, cognitive load, and interface design. Academic medicine 81, 3 (2006), 213--223.Google ScholarGoogle Scholar
  20. Peter A Hancock and Najmedin Ed Meshkati. 1988. Human mental workload. North-Holland.Google ScholarGoogle Scholar
  21. Sandra G Hart and Lowell E Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in psychology 52 (1988), 139--183.Google ScholarGoogle Scholar
  22. Sandra G Hart and Lowell E Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. Advances in psychology 52 (1988), 139--183.Google ScholarGoogle Scholar
  23. Sandra G Hart and Christopher D Wickens. 1990. Workload assessment and prediction. In Manprint. Springer, 257--296.Google ScholarGoogle Scholar
  24. Niels Henze, Enrico Rukzio, and Susanne Boll. 2011. 100,000,000 taps: analysis and improvement of touch performance in the large. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services. ACM, 133--142. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Javier Hernandez, Pablo Paredes, Asta Roseway, and Mary Czerwinski. 2014. Under pressure: sensing stress of computer users. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 51--60. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Joyce Ho and Stephen S Intille. 2005. Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. In CHI '05. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Sture Holm. 1979. A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics (1979), 65--70.Google ScholarGoogle Scholar
  28. Shamsi T Iqbal, Xianjun Sam Zheng, and Brian P Bailey. 2004. Task-evoked pupillary response to mental workload in human-computer interaction. In CHI'04 extended abstracts on Human factors in computing systems. ACM, 1477--1480. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Jeff A Johnson. 1995. A comparison of user interfaces for panning on a touch-controlled display. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM Press/Addison-Wesley Publishing Co., 218--225. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Daniel Kahneman. 1973. Attention and effort. Citeseer.Google ScholarGoogle Scholar
  31. Robert J Kosinski. 2008. A literature review on reaction time. Clemson University 10 (2008).Google ScholarGoogle Scholar
  32. Min Lin, Rich Goldman, Kathleen J Price, Andrew Sears, and Julie Jacko. 2007. How do people tap when walking? An empirical investigation of nomadic data entry. International journal of human-computer studies 65, 9 (2007), 759--769. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. David T Lykken and Peter H Venables. 1971. Direct measurement of skin conductance: A proposal for standardization. Psychophysiology 8, 5 (1971), 656--672.Google ScholarGoogle ScholarCross RefCross Ref
  34. Abhinav Mehrotra, Mirco Musolesi, Robert Hendley, and Veljko Pejovic. 2015. Designing content-driven intelligent notification mechanisms for mobile applications. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 813--824. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Abhinav Mehrotra, Veljko Pejovic, Jo Vermeulen, Robert Hendley, and Mirco Musolesi. 2016. My phone and me: understanding people's receptivity to mobile notifications. In Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, 1021--1032. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Neville Moray. 1979. Models and measures of mental workload. In Mental Workload. Springer, 13--21.Google ScholarGoogle Scholar
  37. Seyed Yaghoub Mousavi, Renae Low, and John Sweller. 1995. Reducing cognitive load by mixing auditory and visual presentation modes. Journal of educational psychology 87, 2 (1995), 319.Google ScholarGoogle ScholarCross RefCross Ref
  38. Sharon Oviatt. 2006. Human-centered design meets cognitive load theory: designing interfaces that help people think. In Proceedings of the 14th annual ACM international conference on Multimedia. ACM, 871--880. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Fred Paas, Juhani E Tuovinen, Huib Tabbers, and Pascal WM Van Gerven. 2003. Cognitive load measurement as a means to advance cognitive load theory. Educational psychologist 38, 1 (2003), 63--71.Google ScholarGoogle Scholar
  40. Fred G Paas. 1992. Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of educational psychology 84, 4 (1992), 429.Google ScholarGoogle ScholarCross RefCross Ref
  41. Fred GWC Paas and Jeroen JG Van Merriënboer. 1994. Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of educational psychology 86, 1 (1994), 122.Google ScholarGoogle ScholarCross RefCross Ref
  42. Fred GWC Paas, Jeroen JG Van Merriënboer, and Jos J Adam. 1994. Measurement of cognitive load in instructional research. Perceptual and motor skills 79, 1 (1994), 419--430.Google ScholarGoogle Scholar
  43. John Palfrey and Urs Gasser. 2013. Born digital: Understanding the first generation of digital natives. Basic Books. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Oskar Palinko, Andrew L Kun, Alexander Shyrokov, and Peter Heeman. 2010. Estimating cognitive load using remote eye tracking in a driving simulator. In Proceedings of the 2010 symposium on eye-tracking research & applications. ACM, 141--144. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Veljko Pejovic and Mirco Musolesi. 2014. InterruptMe: Designing Intelligent Prompting Mechanisms for Pervasive Applications. UbiComp '14 (2014). Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Marc Prensky. 2001. Digital natives, digital immigrants part 1. On the horizon 9, 5 (2001), 1--6.Google ScholarGoogle Scholar
  47. Alireza Sahami Shirazi, Niels Henze, Tilman Dingler, Martin Pielot, Dominik Weber, and Albrecht Schmidt. 2014. Large-scale assessment of mobile notifications. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 3055--3064. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Zhanna Sarsenbayeva, Niels van Berkel, Chu Luo, Vassilis Kostakos, and Jorge Goncalves. 2017. Challenges of situational impairments during interaction with mobile devices. In Proceedings of the 29th Australian Conference on Computer-Human Interaction. ACM, 477--481. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Yu Shi, Natalie Ruiz, Ronnie Taib, Eric Choi, and Fang Chen. 2007. Galvanic skin response (GSR) as an index of cognitive load. In CHI'07 extended abstracts on Human factors in computing systems. ACM, 2651--2656. Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Elizabeth Spelke, William Hirst, and Ulric Neisser. 1976. Skills of divided attention. Cognition 4, 3 (1976), 215--230.Google ScholarGoogle ScholarCross RefCross Ref
  51. Deian Stefan and Danfeng Yao. 2010. Keystroke-dynamics authentication against synthetic forgeries. In Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), 2010 6th International Conference on. IEEE, 1--8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Els Stuyven, Koen Van der Goten, André Vandierendonck, Kristl Claeys, and Luc Crevits. 2000. The effect of cognitive load on saccadic eye movements. Acta psychologica 104, 1 (2000), 69--85.Google ScholarGoogle Scholar
  53. G Henri Ter Hofte. 2007. Xensible interruptions from your mobile phone. In MobileHCI '07. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Lisa M. Vizer. 2013. Different Strokes for Different Folks: Individual Stress Response as Manifested in Typed Text. (2013).Google ScholarGoogle Scholar
  55. Lisa M Vizer, Lina Zhou, and Andrew Sears. 2009. Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies 67, 10 (2009), 870--886. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Frank Wilcoxon. 1945. Individual comparisons by ranking methods. Biometrics bulletin 1, 6 (1945), 80--83.Google ScholarGoogle Scholar
  57. Masakiyo Yamamoto, Satoshi Ukai, Kazuhiro Shinosaki, Ryouhei Ishii, Shunsuke Kawaguchi, Asao Ogawa, Yuko Mizuno-Matsumoto, Norihiko Fujita, Toshiki Yoshimine, and Masatoshi Takeda. 2006. Spatially filtered magnetoencephalographic analysis of cortical oscillatory changes in basic brain rhythms during the Japanese âĂŸShiritoriâĂŹWord Generation Task. Neuropsychobiology 53, 4 (2006), 215--222.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Detection of a Smartphone User's Distraction Based on Typing and Touch Gestures

      Recommendations

      Comments

      Login options

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

      Sign in
      • Published in

        cover image ACM Other conferences
        PervasiveHealth'19: Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare
        May 2019
        475 pages
        ISBN:9781450361262
        DOI:10.1145/3329189

        Copyright © 2019 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 the author(s) 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].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 20 May 2019

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Overall Acceptance Rate55of116submissions,47%
      • Article Metrics

        • Downloads (Last 12 months)16
        • Downloads (Last 6 weeks)4

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader