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Automatic Decision-Making Style Recognition Method Using Kinect Technology.

Yu Guo1,2, Xiaoqian Liu1,2, Xiaoyang Wang1,2

  • 1Institute of Psychology, Chinese Academy of Sciences, Beijing, China.

Frontiers in Psychology
|March 21, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automatic method to recognize decision-making styles using facial data captured by Kinect. Linear regression analysis showed a significant correlation, proving the feasibility of facial analysis for style recognition.

Keywords:
Kinectdecision-making styleface datalinear regressionmachine learning

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Area of Science:

  • Psychology
  • Computer Science
  • Human-Computer Interaction

Background:

  • Somatosensory interaction technology, like Microsoft's Kinect, is increasingly used in diverse fields.
  • Kinect facilitates behavioral data capture, opening avenues for psychological research.
  • Behavioral and psychological correlation analysis is a growing research area.

Purpose of the Study:

  • To propose and evaluate an automatic decision-making style recognition method.
  • To establish a mapping between facial data and decision-making style scores using machine learning.
  • To explore the feasibility of facial analysis for identifying individual decision-making styles.

Main Methods:

  • Collected facial data from 240 subjects using a Kinect camera.
  • Administered questionnaires to obtain individual decision-making style scores.
  • Employed machine learning algorithms (Linear Regression, SVM, Ridge, Bayesian Ridge) to map facial data to scores.
  • Utilized Linear Regression as the primary model for analysis.

Main Results:

  • The Linear Regression model demonstrated the best performance among the tested algorithms.
  • A correlation coefficient of 0.6 was achieved between the model's predictions and questionnaire-based scores.
  • This indicates a medium to higher correlation, validating the approach.

Conclusions:

  • Automatic decision-making style recognition based on facial analysis is feasible.
  • Facial data captured via somatosensory technology can be effectively used for psychological assessments.
  • This research offers a novel, non-invasive method for understanding individual decision-making.