This study quantitatively evaluated whether and how machine learning (ML) models built by data from controlled conditions can fit real-world conditions. This study focused on feature-based models using wearable technology from real-world data collected from young adults, so as to provide insights into the models’ robustness and the specific challenges posed by diverse environmental noise. Feature-based models, particularly XGBoost and Decision Trees, demonstrated considerable resilience, maintaining higher accuracy and reliability across different noise levels. This investigation included an in-depth analysis of transfer learning, highlighting its potential and limitations in adapting models developed from standard datasets, like WESAD, to complex real-world scenarios. Moreover, this study analyzed the distributed feature importance across various physiological signals, such as electrodermal activity (EDA) and electrocardiography (ECG), considering their vulnerability to environmental factors. It was found that integrating multiple physiological data types could significantly enhance model robustness. The results underscored the need for a nuanced understanding of signal contributions to model efficacy, suggesting that feature-based models showed much promise in practical applications.

Tai Chi (TC) practice has been shown to improve both cognitive and physical function in older adults. However, the neural mechanisms underlying the benefits of TC remain unclear. Our primary aims are to explore whether distinct age-related and TC-practice-related relationships can be identified with respect to either temporal or spatial (within/between-network connectivity) differences. This cross-sectional study examined recurrent neural network dynamics, employing an adaptive, data-driven thresholding approach to source-localized resting-state EEG data in order to identify meaningful connections across time-varying graphs, using both temporal and spatial features derived from a hidden Markov model (HMM). Mann–Whitney U tests assessed between-group differences in temporal and spatial features by age and TC practice using either healthy younger adult controls (YACs, n = 15), healthy older adult controls (OACs, n = 15), or Tai Chi older adult practitioners (TCOAs, n = 15). Our results showed that aging is associated with decreased within-network and between-network functional connectivity (FC) across most brain networks. Conversely, TC practice appears to mitigate these age-related declines, showing increased FC within and between networks in older adults who practice TC compared to non-practicing older adults. These findings suggest that TC practice may abate age-related declines in neural network efficiency and stability, highlighting its potential as a non-pharmacological intervention for promoting healthy brain aging. This study furthers the triple-network model, showing that a balancing and reorientation of attention might be engaged not only through higher-order and top-down mechanisms (i.e., FPN/DAN) but also via the coupling of bottom-up, sensory–motor (i.e., SMN/VIN) networks.

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.

This review provides a detailed exploration of the current state of anxiety detection using machine learning (ML), with a focus on both feature-based and end-to-end models. The field has experienced rapid growth, with a significant increase in academic output ne-cessitating an updated review of commonly used ML models and their performance, anxiety-inducing methodologies, data collection conditions, and dataset utilization. Feature-based ML models, such as Support Vector Machines, 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 powerful alternatives, capable of utilizing raw signal directly and handling large datasets. Additionally, this review categorizes stressors into six distinct types – social, mental, physical, emotional, driving, and daily-life stressors – to provide a better overview of methodologies used to elicit anxiety. These are further explored based on whether data were collected under con-trolled in-lab conditions or real world in-the-wild conditions. This review underscores the need for further exploration into model architecture and their suitability for different types of data, advocating for a more nuanced and personalized approach to anxiety detection using machine learning.