Scoping Review of ML Approaches in Anxiety Detection from In-Lab to In-the-Wild

The field of anxiety detection and use of machine learning (ML) has experienced rapid growth necessitating an updated review of commonly used ML models and their performance, anxiety-inducing methodologies, data collection conditions, and dataset utilization. Feature-based ML models have been extensively employed due to their interpretability and simplicity. However, these models require manual feature engineering, which can be labor-intensive and potentially biased. End-to-end deep learning models have emerged as alternatives, capable of utilizing raw signal directly and handling large datasets. This review aims to provide a detailed exploration of anxiety detection using ML, including use of feature-based vs. end-to-end models, a taxonomy of stressors, performance benchmarks, challenges in deployment to real-world scenarios, and generalizability of findings, given limitations in sociodemographic diversity and heterogeneity in the use of validated anxiety measures. A total of 105 eligible papers were retrieved from the Scopus, IEEE Xplore, and PubMed databases. Stressors were categorized into six distinct types—social, mental, physical, emotional, driving, and daily-life stressors—to provide a better overview of methodologies used to elicit anxiety. Papers were organized according to the type of data collection—lab-based or real-world conditions—and characterized through the type of anxiety instrument used, population examined, and classification performance. This review underscores the need for further investigation into model architecture and their suitability for different types of data, limitations in population diversity and representation in existing studies, and advocating for a more nuanced and personalized approach to anxiety detection using machine learning.