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Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations

Author

Listed:
  • Berna Ulutas
  • Firat Ozkan
  • Rafal Michalski

Abstract

Visual inspection is used in many areas due to the potential high costs of inspection error such as injury, fatality, loss of expensive equipment, scrapped items, rework, or failure to procure repeat business. This study presents an application of hidden Markov models (HMM) to fixations’ sequences analysis during visual inspection of front panels in a home appliance facility. The eye tracking data are gathered when quality control operators perform their tasks.The results support the difference between expert and novice operator.Moreover, the article demonstrates fourHMMswith two and three hidden states both for novice and experienced operators and provides analysis and discussion of the outcomes.

Suggested Citation

  • Berna Ulutas & Firat Ozkan & Rafal Michalski, 2020. "Application of hidden Markov models to eye tracking data analysis of visual quality inspection operations," WORking papers in Management Science (WORMS) WORMS/20/11, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    • Handle: RePEc:ahh:wpaper:worms2011
    DOI: 10.1007/s10100-019-00628-x
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    File URL: https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_20_11.pdf
    File Function: Final version, 2020
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    References listed on IDEAS

    as
    1. Jerzy Grobelny & Rafal Michalski, 2017. "Applying hidden Markov models to visual activity analysis for simple digital control panel operations," WORking papers in Management Science (WORMS) WORMS/17/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    2. Rafal Michalski & Jerzy Grobelny, 2016. "An eye tracking based examination of visual attention during pairwise comparisons of a digital product’s package," WORking papers in Management Science (WORMS) WORMS/16/14, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    3. John Liechty & Rik Pieters & Michel Wedel, 2003. "Global and local covert visual attention: Evidence from a bayesian hidden markov model," Psychometrika, Springer;The Psychometric Society, vol. 68(4), pages 519-541, December.
    4. Kristian Lukander & Miika Toivanen & Kai Puolamäki, 2017. "Inferring Intent and Action from Gaze in Naturalistic Behavior: A Review," International Journal of Mobile Human Computer Interaction (IJMHCI), IGI Global, vol. 9(4), pages 41-57, October.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Laurent Joblot & Magnani Florian & Frédéric Rosin & Robert Pellerin & Mario Passalacqua, 2023. "Protocole expérimental visant l'étude de l’IA centrée sur l'humain dans le contexte de l'Industrie 5.0 : Application en réalité augmentée," Post-Print hal-04142374, HAL.
    2. Krzysztof Hankiewicz & Gerhard-Wilhelm Weber, 2020. "Human factors in a contemporary organization," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(2), pages 579-587, June.
    3. Jerzy Grobelny & Rafal Michalski, 2021. "Hidden Markov models for visual processing of marketing leaflets," WORking papers in Management Science (WORMS) WORMS/21/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    4. Jerzy Grobelny & Rafal Michalski & Gerhard-Wilhelm Weber, 2021. "Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic," WORking papers in Management Science (WORMS) WORMS/21/09, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.

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    More about this item

    Keywords

    Human factors; Visual inspection; Human visual behavior; Hidden markov models; Eeye-tracking technology;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • D01 - Microeconomics - - General - - - Microeconomic Behavior: Underlying Principles
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D87 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Neuroeconomics
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • L15 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Information and Product Quality
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

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