Leveraging data complexity Academic Article uri icon

abstract

  • The current ubiquity of information technology has increased variability among users, creating a corresponding need to properly capture and understand these individual differences. This study introduces a novel application of multifractal statistical methods to distinguish users via patterns of variability within high frequency pupillary response behavior (PRB) data collected during computer-based interaction. PRB was measured from older adults, including two groups diagnosed with age-related macular degeneration (AMD) maintaining a range of visual acuities (n = 14), and one visually healthy control group (i.e., disease-free, 20/20--20/32 acuity) (n = 14). Three measures of the multifractal spectrum, the distribution of regularity indices extracted from time series data, distinguished the user groups, including: 1) Spectral Mode; 2) Broadness; and 3) Left Slope. The results demonstrate a clear relationship between the values of these measures and the level of visual capabilities. These analytical techniques leverage the inherent complexity and richness of this high frequency physiological response data, which can be used to meaningfully differentiate individuals whose sensory and cognitive capabilities may be affected by aging and visual impairment. Multifractality analysis provides an objective, quantifiable means of uncovering and examining the underlying signatures in physiological behavior that may account for individual differences in interaction needs and behaviors.

published proceedings

  • ACM Transactions on Computer-Human Interaction

altmetric score

  • 3

author list (cited authors)

  • Moloney, K. P., Jacko, J. A., Vidakovic, B., Sainfort, F., Leonard, V. K., & Shi, B.

citation count

  • 9

complete list of authors

  • Moloney, Kevin P||Jacko, Julie A||Vidakovic, Brani||Sainfort, Fran├žois||Leonard, V Kathlene||Shi, Bin

publication date

  • September 2006