Next Article in Journal
Exploring Emergent Features of Student Interaction within an Embodied Science Learning Simulation
Previous Article in Journal
What Characterizes the Polymodal Media of the Mobile Phone? The Multiple Media within the World’s Most Popular Medium
Article Menu

Export Article

Open AccessArticle
Multimodal Technologies Interact. 2018, 2(3), 38; https://doi.org/10.3390/mti2030038

A Predictive Fingerstroke-Level Model for Smartwatch Interaction

Information Technology Department, King Saud University, Riyadh 12371, Saudi Arabia
Received: 24 May 2018 / Revised: 19 June 2018 / Accepted: 25 June 2018 / Published: 2 July 2018
Full-Text   |   PDF [2477 KB, uploaded 2 July 2018]   |  

Abstract

The keystroke-level model (KLM) is commonly used to predict the time it will take an expert user to accomplish a task without errors when using an interactive system. The KLM was initially intended to predict interactions in conventional set-ups, i.e., mouse and keyboard interactions. However, it has since been adapted to predict interactions with smartphones, in-vehicle information systems, and natural user interfaces. The simplicity of the KLM and its extensions, along with their resource- and time-saving capabilities, has driven their adoption. In recent years, the popularity of smartwatches has grown, introducing new design challenges due to the small touch screens and bimanual interactions involved, which make current extensions to the KLM unsuitable for modelling smartwatches. Therefore, it is necessary to study these interfaces and interactions. This paper reports on three studies performed to modify the original KLM and its extensions for smartwatch interaction. First, an observational study was conducted to characterise smartwatch interactions. Second, the unit times for the observed interactions were derived through another study, in which the times required to perform the relevant physical actions were measured. Finally, a third study was carried out to validate the model for interactions with the Apple Watch and Samsung Gear S3. The results show that the new model can accurately predict the performance of smartwatch users with a percentage error of 12.07%; a value that falls below the acceptable percentage dictated by the original KLM ~21%. View Full-Text
Keywords: keystroke-level model (KLM); fingerstroke-level model (FLM); smartwatch interaction; predictive model keystroke-level model (KLM); fingerstroke-level model (FLM); smartwatch interaction; predictive model
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Al-Megren, S. A Predictive Fingerstroke-Level Model for Smartwatch Interaction. Multimodal Technologies Interact. 2018, 2, 38.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Multimodal Technologies Interact. EISSN 2414-4088 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top