Multimodal Feature Selection for Detecting Mothers' Depression in Dyadic Interactions with their Adolescent Offspring

Depression is the most common psychological disorder, a leading cause of disability world-wide, and a major contributor to inter-generational transmission of psychopathol-ogy within families. To contribute to our understanding of depression within families and to inform modality selection and featur...

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Bibliographic Details
Main Authors: Bilalpur, Maneesh, Hinduja, Saurabh, Cariola, Laura A., Sheeber, Lisa B., Alien, Nick, Jeni, Laszlo A., Morency, Louis-Philippe, Cohn, Jeffrey F.
Format: Conference Proceeding
Language:English
Subjects:
Depression
Feature extraction
Gesture recognition
History
Problem-solving
Production
Psychology
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Summary:Depression is the most common psychological disorder, a leading cause of disability world-wide, and a major contributor to inter-generational transmission of psychopathol-ogy within families. To contribute to our understanding of depression within families and to inform modality selection and feature reduction, it is critical to identify interpretable features in developmentally appropriate contexts. Mothers with and without depression were studied. Depression was defined as history of treatment for depression and elevations in current or recent symptoms. We explored two multimodal feature selection strategies in dyadic interaction tasks of mothers with their adolescent children for depression detection. Modalities included face and head dynamics, facial action units, speech-related behavior, and verbal features. The initial feature space was vast and inter-correlated (collinear). To reduce dimension-ality and gain insight into the relative contribution of each modality and feature, we explored feature selection strategies using Variance Inflation Factor (VIF) and Shapley values. On an average collinearity correction through VIF resulted in about 4 times feature reduction across unimodal and multimodal features. Collinearity correction was also found to be an optimal intermediate step prior to Shapley analysis. Shapley feature selection following VIF yielded best performance. The top 15 features obtained through Shapley achieved 78 % accuracy. The most informative features came from all four modalities sampled, which supports the importance of multimodal feature selection.
ISSN:1949-3045
1949-3045
DOI:10.1109/FG57933.2023.10042796