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Related Experiment Video

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Mood State Detection in Handwritten Tasks Using PCA-mFCBF and Automated Machine Learning.

Juan Arturo Nolazco-Flores1, Marcos Faundez-Zanuy2, Oliver Alejandro Velázquez-Flores1

  • 1School of Engineering and Science, Tecnológico de Monterrey, Monterrey 64849, Mexico.

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PubMed
Summary

This study demonstrates that analyzing handwriting and drawing patterns on tablets can accurately detect mood states like depression and anxiety. The novel machine learning approach achieved high accuracy, offering a new tool for mental health assessment.

Keywords:
SVMautoMLdata augmentationfeature extractionnegative mood states recognition

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Area of Science:

  • Human-Computer Interaction
  • Affective Computing
  • Machine Learning for Mental Health

Background:

  • Detecting mood states like depression, anxiety, and stress is crucial for timely intervention.
  • Traditional methods for mood assessment can be subjective and time-consuming.
  • Digital biomarkers from user interactions offer a promising avenue for objective mood state detection.

Purpose of the Study:

  • To develop and validate a machine learning model for detecting mood states (depression, anxiety, stress) using tablet-based handwriting and drawing data.
  • To evaluate the effectiveness of feature extraction and selection techniques, including Principal Component Analysis (PCA) and modified Fast Correlation-Based Filtering (mFCBF).
  • To compare the performance of the proposed model against state-of-the-art methods.

Main Methods:

  • Extracted temporal, kinematic, statistical, spectral, and cepstral features from pen pressure, displacement, and azimuth data.
  • Applied PCA for orthogonal transformation and mFCBF for feature selection.
  • Utilized the EMOTHAW database for Depression, Anxiety, and Stress Scale (DASS) assessment.
  • Augmented training data and employed automated machine learning with over ten classifiers.

Main Results:

  • Achieved 100% accuracy in detecting two severity grades for all three mood states when using a specific architecture.
  • Demonstrated superior performance compared to existing state-of-the-art methods.
  • Reported specific accuracy rates for detecting the three mood states: 82.5% for depression, 72.8% for anxiety, and 74.56% for stress.

Conclusions:

  • Handwriting and drawing analysis on tablets, combined with advanced machine learning, provides a highly accurate method for mood state detection.
  • The developed model offers precise information for clinical psychologists, potentially improving mental health diagnostics.
  • This approach represents a significant advancement in leveraging digital interaction data for mental well-being.