IDEAS home Printed from https://ideas.repec.org/h/spr/spochp/978-3-031-53092-0_17.html
   My bibliography  Save this book chapter

Bayesian Approach to Multimodal Data in Human Factors Engineering

Author

Listed:
  • Katherina A. Jurewicz

    (Oklahoma State University, Department of Industrial Engineering and Management)

  • David M. Neyens

    (Clemson University, Department of Industrial Engineering)

Abstract

Human behavior is complex, especially when engaging with large or ill-defined systems, and traditional statistical approaches typically cannot directly analyze and interpret human behavioral data. There are several statistical techniques that could be applied to modeling human behavior; however, because of the parallels between Bayesian thinking and how humans interact with complex systems, Bayesian analytical methods may be the most appropriate for modeling and predicting human behavior. This chapter reviews human factors research and experimentation, Bayesian thinking, and demonstrates an example of how to perform a Bayesian statistical analysis of human behavioral data in a human–computer interaction task. The data used in the numerical example comes from an experimental study on 3D gestural human–computer interaction for anesthetic tasks [1]. Anesthesia providers were asked to perform hand gestures to interact with a computer system and perform specific anesthetic tasks, and a Bayesian statistical approach was used to predict intuitive gesture choice based on system factors (e.g., contextual task) and individual factors (e.g., expertise). The data of interest for the Bayesian modeling is the intuitive gesture choice made by the participants which is categorical with many different levels. Thus, the numerical example outlined in this chapter analyzes data that is distributed multinomially.

Suggested Citation

  • Katherina A. Jurewicz & David M. Neyens, 2024. "Bayesian Approach to Multimodal Data in Human Factors Engineering," Springer Optimization and Its Applications,, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-53092-0_17
    DOI: 10.1007/978-3-031-53092-0_17
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:spochp:978-3-031-53092-0_17. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.