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Self-esteem recognition based on gait pattern using Kinect.

Bingli Sun1, Zhan Zhang2, Xingyun Liu1

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

Gait & Posture
|September 15, 2017
PubMed
Summary

Gait patterns from Kinect sensor data can recognize self-esteem. This behavioral assessment offers a supplementary method for measuring self-esteem when self-report questionnaires are not feasible.

Keywords:
Behavioral assessmentGait patternKinectMachine learningSelf-esteem

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Area of Science:

  • Psychology
  • Biomedical Engineering
  • Computer Science

Background:

  • Self-esteem is crucial for mental health.
  • Self-report questionnaires may not always be feasible for assessing self-esteem.
  • Behavioral assessments offer a valuable supplement to traditional self-report measures.

Purpose of the Study:

  • To investigate the potential of using gait data collected by Kinect as an indicator for recognizing self-esteem.
  • To develop and evaluate machine learning models for predicting self-esteem based on gait features.

Main Methods:

  • 178 graduate students without disabilities completed the Rosenberg Self-Esteem Scale (RSS).
  • Gait data were collected using a Kinect sensor while participants walked naturally for two minutes.
  • Machine learning models were trained using preprocessed gait data and extracted behavioral features.

Main Results:

  • The best correlation coefficient between predicted and self-reported self-esteem scores was 0.45 (p<0.001).
  • Gender-specific models showed a correlation of 0.43 for males and 0.59 for females (p<0.001).

Conclusions:

  • Gait patterns captured by Kinect demonstrate fair criterion validity for recognizing self-esteem.
  • The developed gait predicting model serves as a viable supplementary method for self-esteem measurement.