Proceedings: GI 2005

Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction

James Fogarty , Ryan Baker , Scott Hudson

Proceedings of Graphics Interface 2005: Victoria, British Columbia, Canada, 9 - 11 May 2005, 129-136

DOI 10.20380/GI2005.16

  • Bibtex

    @inproceedings{Fogarty:2005:10.20380/GI2005.16,
    author = {Fogarty, James and Baker, Ryan and Hudson, Scott},
    title = {Case studies in the use of ROC curve analysis for sensor-based estimates in human computer interaction},
    booktitle = {Proceedings of Graphics Interface 2005},
    series = {GI 2005},
    year = {2005},
    issn = {0713-5424},
    isbn = {1-56881-265-5},
    location = {Victoria, British Columbia, Canada},
    pages = {129--136},
    numpages = {8},
    doi = {10.20380/GI2005.16},
    publisher = {Canadian Human-Computer Communications Society},
    address = {School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada},
    }

Abstract

Applications that use sensor-based estimates face a fundamental tradeoff between true positives and false positives when examining the reliability of these estimates, one that is inadequately described by the straightforward notion of accuracy. To address this tradeoff, this paper examines the use of Receiver Operating Characteristic (ROC) curve analysis, a method that has a long history but is under-appreciated in the human computer interaction research community. We present the fundamentals of ROC analysis, the use of the A' statistic to compute the area under an ROC curve, and the equivalence of A' to the Wilcoxon statistic. We then present several case studies, framed in the context of our work on human interruptibility, demonstrating how ROC analysis can yield better results than analyses based on accuracy. These case studies compare sensor-based estimates with human performance, optimize a feature selection process for the area under the ROC curve, and examine end-user selection of a desirable tradeoff.