By Topic

KLEM: A Method for Predicting User Interaction Time and System Energy Consumption during Application Design

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Lu Luo ; Carnegie Mellon Univ., Pittsburgh ; Siewiorek, D.P.

The impact of user interactions on the electric energy consumption of a portable computer system and on user efficiency is often not obtainable until after the software application is implemented and deployed on a specific hardware platform. In this paper, we present the keystroke-level energy model (KLEM), a method that can predict the user time and system energy consumption it will take to perform an interactive task at run time during the phase of application design. KLEM is based on the keystroke-level model (KLM), a psychological theory of human cognitive and motor capabilities that can predict execution time for a skilled user. We first create a design story board and define a set of tasks whose KLMs are to be constructed. We then construct KLEM of each task by correlating system activities to the user actions modeled in the corresponding KLM. We obtain the energy profiles of system activities from running a set of user interaction benchmarks on the target hardware platform. To verify KLEM, we conducted a user study of 10 participants on executing an information query task using eight different methods. The user time and system energy of the participants were measured on two popular handheld platforms: a Windows Mobile iPaq and a Palm OS Tungsten. Our experimental results show that KLEM has an average prediction error of 5.6% and 8.8% on user time, and 4.4% and 8.4% on energy consumption on the two platforms, respectively.

Published in:

Wearable Computers, 2007 11th IEEE International Symposium on

Date of Conference:

11-13 Oct. 2007