Classifying mobile eye tracking data with hidden Markov models

Dmitry Kit, Brian Sullivan

Research output: Contribution to conferenceConference Paper

3 Citations (Scopus)

Abstract

Naturalistic eye movement behavior has been measured in a variety of scenarios [15] and eye movement patterns appear indicative of task demands [16]. However, systematic task classification of eye movement data is a relatively recent development [1,3,7]. Additionally, prior work has focused on classification of eye movements while viewing 2D screen based imagery. In the current study, eye movements from eight participants were recorded with a mobile eye tracker. Participants performed five everyday tasks: Making a sandwich, transcribing a document, walking in an office and a city street, and playing catch with a flying disc [14]. Using only saccadic direction and amplitude time series data, we trained a hidden Markov model for each task and classified unlabeled data by calculating the probability that each model could generate the observed sequence. We present accuracy and time to recognize results, demonstrating better than chance performance.
Original languageEnglish
Pages1037-1040
DOIs
Publication statusPublished - 6 Sep 2016
EventMobile HCI: Inferring User Action with Mobile Gaze Tracking - Florence , Italy
Duration: 6 Sep 20169 Sep 2016
http://mobilehci.acm.org/2016/

Conference

ConferenceMobile HCI
CountryItaly
CityFlorence
Period6/09/169/09/16
Internet address

Keywords

  • task classification
  • Machine Learning
  • Mobile eye tracking
  • natural tasks

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  • Cite this

    Kit, D., & Sullivan, B. (2016). Classifying mobile eye tracking data with hidden Markov models. 1037-1040 . Paper presented at Mobile HCI, Florence , Italy. https://doi.org/10.1145/2957265.2965014