Assessing Mental Workload from Skin Conductance and Pupillometry using Wavelets and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Roger Lew and Brian P. Dyre and Terence Soule and 
                 Stuart A. Ragsdale and Steffen Werner",
  title =        "Assessing Mental Workload from Skin Conductance and
                 Pupillometry using Wavelets and Genetic Programming",
  booktitle =    "Proceedings of the Human Factors and Ergonomics
                 Society Annual Meeting",
  year =         "2010",
  volume =       "54",
  number =       "3",
  pages =        "254--258",
  address =      "San Francisco, USA",
  month =        "27 " # sep # " - 1 " # oct,
  organisation = "Human Factors and Ergonomics Society",
  publisher =    "Sage",
  keywords =     "genetic algorithms, genetic programming, augmented
  isbn13 =       "978-0-945289-37-1",
  DOI =          "doi:10.1177/154193121005400315",
  abstract =     "An essential component of augmented cognition (AC) is
                 developing robust methods of extracting reliable and
                 meaningful information from physiological measures in
                 real-time. To evaluate the potential of skin
                 conductance (SC) and pupil diameter (PD) measures, we
                 used a dual-axis pursuit tracking task where the
                 control mappings repeatedly and abruptly rotated
                 90degree throughout the trials to provide an immediate
                 and obvious challenge to proper system control. Using
                 these data, a model-building technique novel to these
                 measures, genetic programming (GP) with scaled symbolic
                 regression and Age Layered Populations (ALPS), was
                 compared to traditional linear discriminant analysis
                 (LDA) for predicting tracking error and control-mapping
                 state. When compared with traditional linear modelling
                 approaches, symbolic regression better predicted both
                 tracking error and control mapping state. Furthermore,
                 the estimates obtained from symbolic regression were
                 less noisy and more robust.",
  notes =        "1 Department of Psychology and Communication Studies.
                 2 Department of Computer Science University of Idaho


Genetic Programming entries for Roger Lew Brian P Dyre Terence Soule Stuart A Ragsdale Steffen Werner