Classifying mobile eye tracking data with hidden Markov models

Dmitry Kit, Brian Sullivan

Research output: Contribution to conferenceConference Paperpeer-review

9 Citations (Scopus)


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
Publication statusPublished - 6 Sept 2016
EventMobile HCI: Inferring User Action with Mobile Gaze Tracking - Florence , Italy
Duration: 6 Sept 20169 Sept 2016


ConferenceMobile HCI
Internet address


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


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