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Related Experiment Video

Updated: Jun 12, 2025

Author Spotlight: Capturing Infant-Caregiver Interactions Through Synchronized Multimodal Data Collection
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Multimodal Feature Selection for Detecting Mothers' Depression in Dyadic Interactions with their Adolescent

Maneesh Bilalpur1, Saurabh Hinduja2, Laura A Cariola3

  • 1Intelligent Systems Program, University of Pittsburgh, Pittsburgh, USA.

IEEE Transactions on Affective Computing
|September 19, 2024
PubMed
Summary
This summary is machine-generated.

Identifying interpretable depression features is crucial for family mental health. Multimodal analysis using Variance Inflation Factor (VIF) and Shapley values achieved 78% accuracy in detecting maternal depression, highlighting the importance of diverse data sources.

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Area of Science:

  • Psychiatry and Psychology
  • Computational Linguistics
  • Human-Computer Interaction

Background:

  • Depression is a prevalent global mental health disorder with significant inter-generational impact.
  • Understanding depression in family contexts requires interpretable features relevant to developmental stages.
  • Accurate depression detection is vital for targeted interventions and family support.

Purpose of the Study:

  • To identify interpretable features for depression detection in mothers interacting with adolescent children.
  • To explore and compare multimodal feature selection strategies for enhanced depression detection accuracy.
  • To assess the effectiveness of Variance Inflation Factor (VIF) and Shapley values in feature reduction and selection.

Main Methods:

  • Studied mothers with and without depression, defining depression by treatment history and symptom severity.
  • Utilized multimodal data including face/head dynamics, facial action units, speech, and verbal content during dyadic interactions.
  • Applied Variance Inflation Factor (VIF) for collinearity correction and Shapley values for feature selection.

Main Results:

  • Variance Inflation Factor (VIF) reduced feature dimensionality by approximately fourfold.
  • Shapley feature selection, applied after VIF correction, demonstrated optimal performance.
  • The top 15 features identified via Shapley analysis achieved 78% accuracy in depression detection.
  • Informative features were derived from all four sampled modalities (face, speech, verbal).

Conclusions:

  • Multimodal feature selection is essential for robust depression detection.
  • Combining VIF for collinearity and Shapley values for feature importance provides an effective strategy.
  • The findings underscore the value of integrating diverse data streams for understanding and detecting maternal depression.