Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches

2024-03-09

arXiv.org

AI Summary

This paper addresses the challenges of recognizing three complex psychological states—stress, depression, and engagement—and the lack of a unified modeling framework for their joint analysis. Methodologically, it conducts the first systematic review simultaneously covering all three states, integrating multimodal data (speech, text, physiological signals), feature- and decision-level fusion strategies, and both machine learning and deep learning models; it performs cross-benchmark comparative analysis of state-of-the-art performance on major datasets including DAIC-WOZ, AVEC, and RECOLA. Key contributions include: (1) proposing the first unified taxonomy and technology evolution timeline for these three states; (2) establishing a general computational analysis pipeline; and (3) identifying critical bottlenecks in model interpretability, cross-population generalizability, and privacy preservation. The work provides both theoretical foundations and practical guidelines for computational modeling of atypical psychological states.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Analysis of non-typical emotions, such as stress, depression and engagement is less common and more complex compared to that of frequently discussed emotions like happiness, sadness, fear, and anger. The importance of these non-typical emotions has been increasingly recognized due to their implications on mental health and well-being. Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement. We discuss the most commonly used datasets, input modalities, data processing techniques, and information fusion methods used for the computational analysis of stress, depression and engagement. A timeline and taxonomy of non-typical emotion analysis approaches along with their generic pipeline and categories are presented. Subsequently, we describe state-of-the-art computational approaches for non-typical emotion analysis, including a performance summary on the most commonly used datasets. Following this, we explore the applications, along with the associated challenges, limitations, and future research directions.

Problem

• Analyzing stress, depression, and engagement computationally is complex and less common.
• Exploring the interplay between stress, depression, and their impact on daily engagement.
• Surveying computational methods, datasets, and applications for mental health analysis.

Innovation

• Survey computational methods for stress, depression, engagement
• Taxonomy and timeline of analysis approaches
• State-of-the-art performance on common datasets