Automatic Personality Assessment through Movement Analysis Articles uri icon

authors

  • DELGADO GOMEZ, DAVID
  • MASO BESGA, ANTONIO EDUARDO
  • AGUADO GARCIA, DAVID
  • RUBIO, VICTOR
  • SÚJAR GARRIDO, AARÓN
  • BAYONA, SOFIA

publication date

  • May 2022

start page

  • 3949

end page

  • 3960

issue

  • 10

volume

  • 20

International Standard Serial Number (ISSN)

  • 1424-3210

Electronic International Standard Serial Number (EISSN)

  • 1424-8220

abstract

  • Obtaining accurate and objective assessments of an individual"s personality is vital in
    many areas including education, medicine, sports and management. Currently, most personality
    assessments are conducted using scales and questionnaires. Unfortunately, it has been observed
    that both scales and questionnaires present various drawbacks. Their limitations include the lack
    of veracity in the answers, limitations in the number of times they can be administered, or cultural
    biases. To solve these problems, several articles have been published in recent years proposing the use
    of movements that participants make during their evaluation as personality predictors. In this work,
    a multiple linear regression model was developed to assess the examinee"s personality based on their
    movements. Movements were captured with the low-cost Microsoft Kinect camera, which facilitates
    its acceptance and implementation. To evaluate the performance of the proposed system, a pilot
    study was conducted aimed at assessing the personality traits defined by the Big-Five Personality
    Model. It was observed that the traits that best fit the model are Extroversion and Conscientiousness.
    In addition, several patterns that characterize the five personality traits were identified. These results
    show that it is feasible to assess an individual"s personality through his or her movements and open
    up pathways for several research.

subjects

  • Statistics

keywords

  • personality assessment; movement; kinect; big-five model