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Soft Computing

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04.01.2022 | Focus

ConTEXT: context-aware adaptive SMS client for drivers to reduce risky driving behaviors

verfasst von: Inayat Khan, Shah Khusro

Erschienen in: Soft Computing | Ausgabe 16/2022

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Abstract

Text messaging while driving has been considered a dangerous activity that may lead to serious injuries and traffic fatalities. Several assistive technologies and solutions have been developed to simplify texting activity. However, due to inconsistent and complex interface design, lack of logical navigational order, lack of context, complicated text-entry layouts, and laborious activities, the existing texting-related activities can lead to accidents. This paper recognized the risky driving patterns using the real-time AutoLog application. Based on this risky driving behavior, we have proposed ConTEXT, a usable SMS client, to overcome the issues pertaining to the usability of textual activities on smartphones while driving. ConTEXT application is evaluated both empirically as well as through real-time AutoLog application. We have collected data from 117 drivers through a questionnaire. The results show that the data are found reliable also alpha scores for all factors seem internally consistent as it ranges from 0.70 to 0.79 which is good. Similarly, we have reported Principal Component Factor Analysis and found satisfied and appropriate as the Eigenvalue for all the factors is greater than zero. Furthermore, results obtained from the AutoLog dataset show an improved user experience, better control over the touch screen with minimum visual, physical, and mental load.

Vorheriger Artikel Deep ensembling for perceptual image quality assessment

Nächster Artikel Design of robust deep learning-based object detection and classification model for autonomous driving applications

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Abascal J, Nicolle C (2005) Moving towards inclusive design guidelines for socially and ethically aware HCI. Interact Comput 17(5):484–505CrossRef

Adipat B, Zhang D (2005) Interface design for mobile applications. In: AMCIS 2005 proceedings, pp. 494

Akiki PA, Bandara AK, Yu Y (2015) Adaptive model-driven user interface development systems. ACM Comput Surv 47(1):1–33CrossRef

Albert G et al (2016) Which smartphone’s apps may contribute to road safety? An AHP model to evaluate experts’ opinions. Transp Policy 50:54–62CrossRef

Ali S et al (2017) Smartontosensor: ontology for semantic interpretation of smartphone sensors data for context-aware applications. J Sens 2017:1–26CrossRef

Alm H, Nilsson L (1994) Changes in driver behaviour as a function of handsfree mobile phones—a simulator study. Accid Anal Prev 26(4):441–451CrossRef

Angesleva J, Reynolds C, O'Modhrain S (2004) Emotemail. In: International conference on computer graphics and interactive techniques: ACM SIGGRAPH 2004 Posters

Atchley P, Atwood S, Boulton A (2011) The choice to text and drive in younger drivers: behavior may shape attitude. Accid Anal Prev 43(1):134–142CrossRef

Bongartz S et al (2012) Adaptive user interfaces for smart environments with the support of model-based languages. In: International joint conference on ambient intelligence. Springer

Brown PJ (1995) The stick-e document: a framework for creating context-aware applications. Electron Publ-Chichester 8:259–272

Brown PJ, Bovey JD, Chen X (1997) Context-aware applications: from the laboratory to the marketplace. IEEE Pers Commun 4(5):58–64CrossRef

Buschek D, Hassib M, Alt F (2018) Personal mobile messaging in context: chat augmentations for expressiveness and awareness. ACM Trans Comput-Huma Interact (TOCHI) 25(4):1–33CrossRef

Caird JK et al (2014) A meta-analysis of the effects of texting on driving. Accid Anal Prev 71:311–318CrossRef

Caird JK et al (2018) Does talking on a cell phone, with a passenger, or dialing affect driving performance? An updated systematic review and meta-analysis of experimental studies. Hum Factors 60(1):101–133CrossRef

Cooper JM, Ingebretsen H, Strayer DL (2014) Mental workload of common voice-based vehicle interactions across six different vehicle systems (technical report). AAA Foundation for Traffic Safety, Washington, DC

Corcho O et al. (2003) Automatic generation of knowledge portals for intranets and extranets. LNCS 2870. Springer-Verlag

Cortina JM (1993) What is coefficient alpha? An examination of theory and applications. J Appl Psychol 78(1):98CrossRef

Costello AB, Osborne J (2005) Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Pract Assess Res Eval 10(1):7

Cronbach LJ (1951) Coefficient alpha and the internal structure of tests. Psychometrika 16(3):297–334MATHCrossRef

Fabri M, Moore D, Hobbs D (2005) Empathy and enjoyment in instant messaging. In: Proceedings of 19th British HCI group annual conference (HCI2005), Edinburgh, UK

Fitch, Gregory M et al (2013) The impact of hand-held and hands-free cell phone use on driving performance and safety-critical event risk. No. DOT HS 811 757

Gajos K, Weld DS (2004) SUPPLE: automatically generating user interfaces. In: Proceedings of the 9th international conference on Intelligent user interfaces

Gajos KZ, Long JJ, Weld DS (2006) Automatically generating custom user interfaces for users with physical disabilities. In: Assets

Gamecho B et al (2015) Automatic generation of tailored accessible user interfaces for ubiquitous services. IEEE Trans Hum-Mach Syst 45(5):612–623CrossRef

He J et al (2014) Texting while driving: Is speech-based text entry less risky than handheld text entry? Accid Anal Prev 72:287–295CrossRef

Hervás R, Bravo J (2011) Towards the ubiquitous visualization: adaptive user-interfaces based on the semantic web. Interact Comput 23(1):40–56CrossRef

Hong J-H, Yang S-I, Cho S-B (2010) ConaMSN: A context-aware messenger using dynamic Bayesian networks with wearable sensors. Expert Syst Appl 37(6):4680–4686CrossRef

Hosking SG, Young KL, Regan MA (2009) The effects of text messaging on young drivers. Hum Factors 51(4):582–592CrossRef

Hussain J et al (2018) Model-based adaptive user interface based on context and user experience evaluation. J Multimodal User Interfaces 12(1):1–16CrossRef

Best driving apps (2018) Social Halo Media | Aug 13, 2018 | Blog, Driving. https://varsitydrivingacademy.com/best-driving-appsof-2018/. Accessed 17 Apr 2021

Khan I et al. (2016) Sensors are power hungry: an investigation of smartphone sensors impact on battery power from life logging perspective. Bahria Univ J Inf Commun Technolgies (BUJICT) 9(2):08–19

Khan I, Khusro S (2020) Towards the design of context-aware adaptive user interfaces to minimize drivers’ distractions. Mob Inf Syst 2020:1–23

Khan I et al (2017) Vehicular lifelogging: issues, challenges, and research opportunities. J Inform Commun Technol Robot Appl 8(2):30–37

Khan A, Khusro S, Alam I (2018) Blindsense: an accessibility-inclusive universal user interface for blind people. Eng Technol Appl Sci Res 2020(2):2775–2784CrossRef

Khan I et al (2020) AutoLog: toward the design of a vehicular lifelogging framework for capturing, storing, and visualizing LifeBits. IEEE Access 8:136546–136559CrossRef

Khan A et al. (2018) TetraMail: a usable email client for blind people. Univ Access Inf Soc pp. 1–20

Khan I, Ali S, Khusro S (2019) Smartphone-based lifelogging: An investigation of data volume generation strength of smartphone sensors. In: International conference on simulation tools and techniques. Springer

Khan I, Khusro S, Alam I (2019) Smartphone distractions and its effect on driving performance using vehicular lifelog dataset. In: 2019 international conference on electrical, communication, and computer engineering (ICECCE). IEEE.

Korpipää P et al. (2004) Utilising context ontology in mobile device application personalisation. In: Proceedings of the 3rd international conference on mobile and ubiquitous multimedia

Lee VK, Champagne CR, Francescutti LH (2013) Fatal distraction: cell phone use while driving. Can Fam Physician 59(7):723–725

Limbourg Q et al. (2004) USIXML: a language supporting multi-path development of user interfaces. In: IFIP international conference on engineering for human-computer interaction. Springer

MailOnline, J.O.C.F. (2015) Drivers are more distracted than ever before - and taking your eyes off the road for just 2 seconds increases accident risk 24 times. https://www.dailymail.co.uk/sciencetech/article-3000917/Drivers-distractedtaking-eyes-road-just-2-seconds-increases-accident-risk-24-times.html

McGinn MC (2014) Predicting factors for use of texting and driving applications and the effect on changing behaviors. Southern Illinois University at Edwardsville

Newell AF, Gregor P (2000) User sensitive inclusive design—in search of a new paradigm. In: Proceedings on the 2000 conference on Universal Usability

Oviedo-Trespalacios O et al (2019) Can our phones keep us safe? A content analysis of smartphone applications to prevent mobile phone distracted driving. Transp Res F: Traffic Psychol Behav 60:657–668CrossRef

Paterno F et al (2008) Authoring pervasive multimodal user interfaces. Int J Web Eng Technol 4(2):235–261CrossRef

Paterno F, Santoro C, Spano LD (2009) MARIA: a universal, declarative, multiple abstraction-level language for service-oriented applications in ubiquitous environments. ACM Trans Comput-Hum Interact (TOCHI) 16(4):19CrossRef

Peissner M et al. (2012) MyUI: generating accessible user interfaces from multimodal design patterns. In: Proceedings of the 4th ACM SIGCHI symposium on Engineering interactive computing systems. ACM

Persad U, Langdon P, Clarkson J (2007) Characterising user capabilities to support inclusive design evaluation. Univ Access Inf Soc 6(2):119–135CrossRef

Petrie H, Bevan N (2009) The evaluation of accessibility, usability, and user experience. In: The universal access handbook, CRC Press. Heslington Hall, University of York, Heslington, York, pp 1–16

Plos O et al (2012) A universalist strategy for the design of assistive technology. Int J Ind Ergon 42(6):533–541CrossRef

Pong KC, Wang CA, Hsu SH, Gam IM (2014) Affecting chatting behavior by visualizing atmosphere of conversation. In: CHI'14 extended abstracts on human factors in computing systems. pp. 2497–2502

Riemer-Reiss ML, Wacker RR (2000) Factors associated with assistive technology discontinuance among individuals with disabilities. J Rehabil 66(3):44–50

Rohrer C, Design UE (2009) User experience research methods in 3D: What to use when and how to know you’re right. Keynote presentation at BayCHI–The San Francisco chapter of ACM SIGCHI

Rovers A, van Essen HA (2004) HIM: a framework for haptic instant messaging. In: CHI'04 extended abstracts on human factors in computing systems

Rumschlag G et al (2015) The effects of texting on driving performance in a driving simulator: the influence of driver age. Accid Anal Prev 74:145–149CrossRef

Ryan N, Pascoe J, Morse D (1999) Enhanced reality fieldwork: the context aware archaeological assistant. In: Dingwall L, Exon S, Gaffney V, Laflin S, van Leusen M (eds) Archaeology in the age of the internet. CAA97. Computer applications and quantitative methods in archaeology. Proceedings of the 25th anniversary conference, University of Birmingham, April 1997 (BAR International Series 750). Archaeopress, Oxford, pp 269–274

Schilit WN (1995) A system architecture for context-aware molbil computing. Citeseer, Columbia University

Schilit B, Adams N, Want R (1994) Context-aware computing applications. In: IEEE workshop on mobile computing systems and applications. Proceedings of the workshop on mobile computing systems and applications. Google Scholar google scholar digital library digital library

Tchankue P, Wesson J, Vogts D (2011) The impact of an adaptive user interface on reducing driver distraction. In: Proceedings of the 3rd international conference on automotive user interfaces and interactive vehicular applications

Tchankue P, Wesson J, Vogts D (2012) Designing a mobile, context-aware in-car communication system. In: Proceedings of SATNAC 2012 on limited range communication, pp 1–6

Tsetserukou D et al. (2009) iFeel_IM! Emotion enhancing garment for communication in affect sensitive instant messenger. In Symposium on Human Interface. Springer

Walsh SP et al (2008) Dialling and driving: Factors influencing intentions to use a mobile phone while driving. Accid Anal Prev 40(6):1893–1900CrossRef

Wang Y et al. (2013) Sensing vehicle dynamics for determining driver phone use. In: Proceeding of the 11th annual international conference on Mobile systems, applications, and services. ACM

Wilson FA, Stimpson JP (2010) Trends in fatalities from distracted driving in the United States, 1999 to 2008. Am J Public Health 100(11):2213–2219CrossRef

World Health Organization (2011) Mobile phone use: a growing problem of driver distraction

Yeo Z (2008) Emotional instant messaging with KIM. In CHI'08 extended abstracts on Human factors in computing systems. pp. 3729–3734

Zimmermann A, Lorenz A, Oppermann R (2007) An operational definition of context. In: International and interdisciplinary conference on modeling and using context. Springer

Zinbarg RE et al (2005) Cronbach’s α, Revelle’s β, and McDonald’s ω H: their relations with each other and two alternative conceptualizations of reliability. Psychometrika 70(1):123–133MathSciNetMATHCrossRef

Titel: ConTEXT: context-aware adaptive SMS client for drivers to reduce risky driving behaviors
verfasst von: Inayat Khan
Shah Khusro
Publikationsdatum: 04.01.2022
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 16/2022
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-021-06705-1

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