Anxiety Detection

2026

1. 

   Journal AnxietyFaceTrack: A Smartphone-Based Non-Intrusive Approach for Detecting State Anxiety Using Facial Features

   Nilesh Kumar Sahu, Snehil Gupta, and Haroon R Lone

   IEEE Transactions on Affective Computing, 2026

   Abs DOI

   Social Anxiety Disorder (SAD) is a widespread mental health condition, yet its lack of objective markers hinders timely detection and intervention. While previous research has focused on behavioral and non-verbal markers of SAD in structured activities (e.g., speeches or interviews), these settings fail to replicate in real-world, unstructured social interactions. Identifying non-verbal markers in naturalistic, unstaged environments is essential for developing ubiquitous and non-intrusive monitoring solutions. To address this gap, we present AnxietyFaceTrack, a study leveraging facial video analysis to detect state anxiety in unstaged social settings. A cohort of 91 participants engaged in a social setting with unfamiliar individuals, and their facial videos were recorded using a low-cost smartphone camera. We examined facial features, including eye movements, head position, facial landmarks, and facial action units, and used self-reported survey data to establish ground truth for multiclass (anxious, neutral, non-anxious) and binary (e.g., anxious vs. neutral) classifications. Our results demonstrate that a Random Forest classifier trained on the top 20% of features achieved the highest accuracy of 91.0% for multiclass classification and an average accuracy of 92.33% across binary classifications. Notably, head position and facial landmarks yielded the best performance for individual facial regions, achieving 85.0% and 88.0% accuracy, respectively, in multiclass classification, and 89.66% and 91.0% accuracy, respectively, across binary classifications. Post-hoc analysis identified head rotation, facial edge features, and eye landmarks as key contributors to the detection of anxiety. This study introduces a non-intrusive, cost-effective solution that can be seamlessly integrated into everyday smartphones for continuous anxiety monitoring, offering a promising pathway for early detection and intervention.

2025

1. 

   Journal MAD: A Multimodal Physiological and Self-Reported Dataset for Anxiety Research from a Low-to-Middle-Income Country

   Nilesh Sahu, Snehil Gupta, and Haroon R. Lone

   Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2025

   Abs DOI

   Wearable sensors provide a promising approach for monitoring physiological parameters, offering valuable insights into mental health conditions, including Social Anxiety Disorder (SAD). Early detection of SAD using physiological data can facilitate timely interventions, yet the development of robust anxiety detection models requires high-quality datasets. While real-world studies provide ecological validity, controlled studies ensure structured, high-quality data with minimal missing values, making them ideal for developing generalized and personalized anxiety detection models.; AB@Existing publicly available datasets related to anxiety research are limited to developed nations and focus on one or two anxiety-inducing activities. However, cultural differences significantly influence how anxiety is experienced and expressed, highlighting the need for datasets from diverse populations. This work presents MAD, a novel dataset collected in a low-to-middle-income country that addresses this gap. Our study involved participants engaging in three anxiety-inducing activities—speech, group discussion, and interview—each structured into three phases: anticipation, performance, and reflection. Physiological data were collected using wearable sensors, including electrocardiogram, electrodermal activity, and photoplethysmography, along with self-reported anxiety levels.; AB@Our dataset (N = 97) is unique in its inclusion of multiple anxiety-inducing activities, comprehensive phase-wise assessment, and representation of an underrepresented population. It provides a valuable resource for developing generalizable anxiety detection models, designing personalized interventions, and studying cultural variations in anxiety responses. By making MAD available, we aim to facilitate future research in machine learning-based mental health analysis, cross-cultural studies, and privacy-preserving anxiety detection approaches.

2. 

   Conference Exploring Heart Rate Variability and Heart Rate Dynamics Using Wearables Before, During, and After Speech Activity: Insights from a Controlled Study in a Low-Middle-Income Country

   Nilesh Kumar Sahu, Snehil Gupta, and Haroon R. Lone

   In Proceedings of the ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies, 2025

   Abs DOI

   Conventional methods for diagnosing Social Anxiety Disorder (SAD), such as clinical interviews and self-reported questionnaires, often face accessibility barriers and subjective biases, underscoring the need for objective physiological markers. This study investigates heart rate (HR) and heart rate variability (HRV) as potential indicators of SAD by analyzing cardiovascular responses to anxiety-inducing speech tasks across four distinct phases: baseline, anticipation, speech activity, and reflection.In a controlled laboratory setting, we analyzed data from 51 participants and found that HRV decreased and HR increased during the anticipation and speech activity phases compared to baseline, while the reflection phase showed a reversal, with HRV increasing and HR decreasing. Participants with SAD exhibited lower HRV, higher HR, and greater self-reported anxiety than non-SAD participants across all phases. These findings enhance our understanding of the physiological signatures of social anxiety and have implications for developing wearable-based monitoring systems for SAD detection and intervention. To support further research, we also release an HRV dataset capturing multi-phase anxiety responses, advancing physiological-based mental health assessment.

2024

1. 

   Workshop Eyes Speak Louder: Harnessing Deep Features From Low-Cost Camera Video for Anxiety Detection

   Nandigramam Sai Harshit, Nilesh Kumar Sahu, and Haroon R Lone

   In Proceedings of the 10th Workshop on Body-Centric Computing Systems, colocated with ACM MobiSys 2024, 2024

   Abs DOI

   Social anxiety is a common mental health disorder, affecting approximately 36% of the world’s population. Currently, diagnosing social anxiety disorder relies on clinical interviews and self-reported questionnaires, which are prone to subjective biases. Discovering non-intrusive, objective markers of anxiety, such as eye gaze patterns, could enhance current diagnostic approaches and facilitate early assessment, thereby reducing the treatment gap. Previous research has explored predicting anxiety states using physiological signals and audio data, but conclusive results remain elusive. In this study, we propose leveraging eye gaze features to predict one’s anxiety levels using deep eye gaze features. One hundred eleven participants engaged in an anxiety provoking speech activity were recorded using a low-cost smartphone camera. Further, we used a variational autoencoder to extract deep eye gaze features from the raw video features, which were then fed into machine-learning models to predict participant’s anxiety levels. Our result shows that the Random Forest classification model achieved the highest average accuracy, precision, recall, & F1 score of 100% (averaged over five folds), suggesting its capability to detect participant’s anxiety in real-world settings.

2. 

   Journal Wearable Technology Insights: Unveiling Physiological Responses During Three Different Socially Anxious Activities

   Nilesh Kumar Sahu, Snehil Gupta, and Haroon R Lone

   ACM Journal on Computing and Sustainable Societies, 2024

   Abs DOI

   Wearable technology holds promise for monitoring and managing Social Anxiety Disorder (SAD), yet the absence of clear biomarkers specific to SAD hampers its effectiveness. This paper explores this issue by presenting a study investigating variances in heart rate, heart rate variability, and skin conductance between socially anxious and non-anxious individuals. One hundred eleven non-clinical student participants participated in groups of three in three anxiety-provoking activities (i.e., speech, group discussion, and interview) in a controlled lab-based study. During the study, electrocardiogram (ECG) and electrodermal activity (EDA) signals were captured via on-body electrodes. During data analysis, participants were divided into four groups based on their self-reported anxiety level (“None”, “mild”, “moderate”, and “severe”). Between-group analysis shows that discriminating ECG features (i.e., HR and MeanNN) could identify anxious individuals during anxiety-provoking activities, while EDA could not. Moreover, the discriminating ECG features improved the classification accuracy of anxious and non-anxious individuals in different machine-learning techniques. The findings need to be further scrutinized in real-world settings for the generalizability of the results.

3. 

   Journal Unveiling Social Anxiety: Analyzing Acoustic and Linguistic Traits in Impromptu Speech within a Controlled Study

   Nilesh Kumar Sahu, Manjeet Yadav, and Haroon R Lone

   ACM Journal on Computing and Sustainable Societies, 2024

   Abs DOI

   Early detection and treatment of Social Anxiety Disorder (SAD) is crucial. However, current diagnostic methods have several drawbacks, including being time-consuming for clinical interviews, susceptible to emotional bias for self-reports, and inconclusive for physiological measures. Our research focuses on a digital approach using acoustic and linguistic features extracted from participants’ “speech” for diagnosing SAD. Our methodology involves identifying correlations between extracted features and SAD severity, selecting the effective features, and comparing classical machine learning and deep learning methods for predicting SAD. Our results demonstrate that both acoustic and linguistic features outperform deep learning approaches when considered individually. Logistic Regression proves effective for acoustic features, while Random Forest excels with linguistic features, achieving the highest accuracy of 85.71%. Our findings pave the way for non-intrusive SAD diagnosing that can be used conveniently anywhere, facilitating early detection.