Utilizing linguistically enhanced keystroke dynamics to predict typist cognition and demographics

David Guy Brizan, Adam Goodkind, Patrick Koch, Kiran Balagani, Vir V. Phoha, Andrew Rosenberg

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Abstract Entering information on a computer keyboard is a ubiquitous mode of expression and communication. We investigate whether typing behavior is connected to two factors: the cognitive demands of a given task and the demographic features of the typist. We utilize features based on keystroke dynamics, stylometry, and "language production", which are novel hybrid features that capture the dynamics of a typists linguistic choices. Our study takes advantage of a large data set (∼350 subjects) made up of relatively short samples (∼450 characters) of free text. Experiments show that these features can recognize the cognitive demands of task that an unseen typist is engaged in, and can classify his or her demographics with better than chance accuracy. We correctly distinguish High vs. Low cognitively demanding tasks with accuracy up to 72.39%. Detection of non-native speakers of English is achieved with F1=0.462 over a baseline of 0.166, while detection of female typists reaches F1=0.524 over a baseline of 0.442. Recognition of left-handed typists achieves F1=0.223 over a baseline of 0.100. Further analyses reveal that novel relationships exist between language production as manifested through typing behavior, and both cognitive and demographic factors.

Original languageEnglish (US)
Article number1959
Pages (from-to)57-68
Number of pages12
JournalInternational Journal of Human Computer Studies
Volume82
DOIs
StatePublished - Jun 14 2015

Keywords

  • Cognitive load recognition
  • Demography recognition
  • Keystroke dynamics
  • Stylometry
  • Typing production

ASJC Scopus subject areas

  • Software
  • Human Factors and Ergonomics
  • Education
  • Engineering(all)
  • Human-Computer Interaction
  • Hardware and Architecture

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