short-paper

Enhancing KLM (keystroke-level model) to fit touch screen mobile devices

Authors:

MobileHCI '14: Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services

Pages 283 - 286

https://doi.org/10.1145/2628363.2628385

Published: 23 September 2014 Publication History

Get Access

Abstract

This PVA-shortpaper introduces an enhancement to the Keystroke-Level Model (KLM) by extending it with three new operators to describe interactions on mobile touchscreen devices. Based on Fitts's Law we modelled a performance measure estimate equation for each common touch screen interaction. Three prototypes were developed to serve as a test environment in which to validate Fitts's equations and estimate the parameters for these interactions. A total of 3090 observations were made with a total of 51 users. While the studies confirmed each interaction fitted well to Fitts's Law for most interactions, it was noticed that Fitts's Law does not fit well for interactions with an Index of Difficulty exceeding 4 bits, highlighting a possible maximum comfortable stretch. Based on results, the following approximate movement times for KLM are suggested: 70ms for a short untargeted swipe, 200ms for a half-screen sized zoom, and 80ms for an icon pointing from a home position. These results could be used by developers of mobile phone and tablet applications to describe tasks as a sequence of the operators used and to predict user interaction times prior to creating prototypes.

References

[1]

Card, S. K., Moran, T. P. and Newell, A. (1983): The Psychology of Human-Computer Interaction. Erlbaum.

Digital Library

Google Scholar

[2]

Dunlop, M. D. and Crossan, A. (2000). Predictive Text Entry Methods for Mobile Phones. Personal Technologies. 41. 134--143.

Google Scholar

[3]

Fitts's, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. J. Experimental Psychology. 47(6). 381--391.

Crossref

Google Scholar

[4]

Holleis, P., Otto, F., Hussmann, H., and Schmidt, A. (2007). Keystroke-level model for advanced mobile phone interaction. Proc. CHI 2007. 1505--1514.

Digital Library

Google Scholar

[5]

How, Y. and Kan, M. Y. (2005). Optimizing Predictive Text Entry for Short Message Service on Mobile Phones. Proc. HCI International 2005.

Google Scholar

[6]

Luo, L. and John, B. E. (2005). Predicting Task Execution Time on Handheld Devices Using the Keystroke-Level Model. Proc CHI 2005. 1605--1608.

Digital Library

Google Scholar

[7]

Pavlovych, A., Stuerzlinger, W. (2004). Model for Nonexpert Text Entry Speed on 12-button Phone Keypads. Proc. CHI 2004. 351--358.

Digital Library

Google Scholar

[8]

MacKenzie, I. S. (1992). Fitts's' law as a research and design tool in human-computer interaction. Human-Computer Interaction. 7. 91--139.

Digital Library

Google Scholar

[9]

Mori, R., Matsunobe, T., and Yamaoka, T. (2003). A Task Operation Prediction Time Computation Based on GOMS-KLM Improved for the Cellular Phone and the Verification of that Validity. Proc. ADC 2003.

Google Scholar

[10]

Myung, R. (2004). Keystroke-level analysis of Korean text entry methods on mobile phones. Int. J. HumanComputer Studies. 60(5), 545--563.

Crossref

Google Scholar

[11]

Teo, L. and John, B. E. (2006). Comparisons of Keystroke-Level Model Predictions to Observed Data. Extended Abstracts of CHI 2006. 1421--1426.

Digital Library

Google Scholar

[12]

Tran, J. J., Trewin, S., Swart, C., John, B. E. and Thomas, J. C. (2013). Exploring pinch and spread gestures on mobile devices. MobileHCI 2013. 151--16.

Digital Library

Google Scholar

[13]

Hoggan, E., Nacenta, M., Kristensson, P. O., Williamson, J., Oulasvirta, A., and Lehtiö, A. (2013). Multi-touch pinch gestures: performance and ergonomics. Proc. ACM 2013. 219--222.

Digital Library

Google Scholar

Cited By

View all

Уженцева АМерзлякова Е(2024)Адаптация модели GOMS для мобильных приложенийСовременные инновации, системы и технологии - Modern Innovations, Systems and Technologies10.47813/2782-2818-2024-4-2-0230-02414:2(0230-0241)Online publication date: 27-May-2024
https://doi.org/10.47813/2782-2818-2024-4-2-0230-0241
Torkamaan H(2023)Mood Measurement on SmartphonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808647:1(1-35)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580864
Vidmanov DAlfimtsev A(2023)MARLMUI: Multi-Agent Reinforcement Learning Approach in Mobile Adaptive User Interface2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)10.1109/REEPE57272.2023.10086785(1-5)Online publication date: 16-Mar-2023
https://doi.org/10.1109/REEPE57272.2023.10086785
Show More Cited By

Index Terms

Enhancing KLM (keystroke-level model) to fit touch screen mobile devices
1. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

In-Car Touch Screen Interaction: Comparing Standard, Finger-Specific and Multi-Finger Interaction
PerDis '15: Proceedings of the 4th International Symposium on Pervasive Displays

In this paper, we explore a novel interaction technique for the automotive domain, distinguishing between different fingers when interacting with a touch screen, and compare it against standard and multi-finger gesture interaction. We conducted a pilot ...
Identifying unintentional touches on handheld touch screen devices
DIS '12: Proceedings of the Designing Interactive Systems Conference

Accidental triggering of unwanted interaction when using a handheld touch screen device is a problem for many users. Accidental touches on capacitive touch screen based mobile telephones were analyzed in a user test. Patterns that are characteristic of ...
A leap for touch: proximity sensitive touch targets in cars
AutomotiveUI '15: Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

Combining touch screen technology with mid-air gestures into a unified input modality has potential to improve interaction with touch interfaces in cars. Moreover, target objects on a touch screen can be adapted to the proximity of a users' finger in ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MobileHCI '14: Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services

September 2014

664 pages

ISBN:9781450330046

DOI:10.1145/2628363

Conference Chairs:
Aaron Quigley
University of St. Andrews, UK
,
Sara Diamond
OCAD University, Canada
,
Program Chairs:
Pourang Irani
University of Manitoba, Canada
,
Sriram Subramanian
University of Bristol, UK

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

MobileHCI '14

Sponsor:

SIGCHI

MobileHCI '14: 16th International Conference on Human-Computer Interaction with Mobile Devices and Services

September 23 - 26, 2014

ON, Toronto, Canada

Acceptance Rates

MobileHCI '14 Paper Acceptance Rate 35 of 124 submissions, 28%;

Overall Acceptance Rate 202 of 906 submissions, 22%

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
804
Total Downloads

Downloads (Last 12 months)46
Downloads (Last 6 weeks)5

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Уженцева АМерзлякова Е(2024)Адаптация модели GOMS для мобильных приложенийСовременные инновации, системы и технологии - Modern Innovations, Systems and Technologies10.47813/2782-2818-2024-4-2-0230-02414:2(0230-0241)Online publication date: 27-May-2024
https://doi.org/10.47813/2782-2818-2024-4-2-0230-0241
Torkamaan H(2023)Mood Measurement on SmartphonesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35808647:1(1-35)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3580864
Vidmanov DAlfimtsev A(2023)MARLMUI: Multi-Agent Reinforcement Learning Approach in Mobile Adaptive User Interface2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)10.1109/REEPE57272.2023.10086785(1-5)Online publication date: 16-Mar-2023
https://doi.org/10.1109/REEPE57272.2023.10086785
Ritter FYeh MStager SMcDermott AWeyhrauch P(2023)The Effect of Task Fidelity on Learning Curves: A Synthetic AnalysisInternational Journal of Human–Computer Interaction10.1080/10447318.2022.216186339:11(2253-2267)Online publication date: 29-Jan-2023
https://doi.org/10.1080/10447318.2022.2161863
Xia HGlueck MAnnett MWang MWigdor D(2022)Iteratively Designing Gesture Vocabularies: A Survey and Analysis of Best Practices in the HCI LiteratureACM Transactions on Computer-Human Interaction10.1145/350353729:4(1-54)Online publication date: 5-May-2022
https://dl.acm.org/doi/10.1145/3503537
Degen H(2022) Big I Notation to Estimate the Interaction Complexity of Interaction Concepts International Journal of Human–Computer Interaction10.1080/10447318.2021.200469938:16(1504-1528)Online publication date: 19-Jan-2022
https://doi.org/10.1080/10447318.2021.2004699
Nikula SMartinie CPalanque PHekkala JLatvala OHalunen K(2022)Models-Based Analysis of Both User and Attacker Tasks: Application to EEVEHACHuman-Centered Software Engineering10.1007/978-3-031-14785-2_5(70-89)Online publication date: 24-Aug-2022
https://dl.acm.org/doi/10.1007/978-3-031-14785-2_5
Cabric FDubois ESerrano M(2021)Extension de KLM au Pointage du Regard et Validation Gestuelle dans HoloLensProceedings of the 32nd Conference on l'Interaction Homme-Machine10.1145/3450522.3451239(1-9)Online publication date: 13-Apr-2021
https://dl.acm.org/doi/10.1145/3450522.3451239
Cabric FDubois ESerrano M(2021)A Predictive Performance Model for Immersive Interactions in Mixed Reality2021 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)10.1109/ISMAR52148.2021.00035(202-210)Online publication date: Oct-2021
https://doi.org/10.1109/ISMAR52148.2021.00035
Guo SLiu SLiang CXie FZheng S(2021)An execution time prediction model for crew information processing in new special vehiclesE3S Web of Conferences10.1051/e3sconf/202125302054253(02054)Online publication date: 6-May-2021
https://doi.org/10.1051/e3sconf/202125302054
Show More Cited By

View Options

Login options

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

Cited By

Index Terms

Recommendations

In-Car Touch Screen Interaction: Comparing Standard, Finger-Specific and Multi-Finger Interaction

Identifying unintentional touches on handheld touch screen devices

A leap for touch: proximity sensitive touch targets in cars