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Researchers developed a smart contact lens that measures serotonin levels in tears, enabling noninvasive and continuous monitoring of stress biomarkers.
This paper presents an AI-powered, culturally aware chatbot for stress detection and wellness support among Pakistani university students, using Random Forest for classification with 89.09% accuracy and integrating an open-source LLM for conversational support.
This paper introduces the Individual Conformal Coupling Monitor (ICCM), a pre-inference method to detect structural ambiguity in wearable stress classification, improving safety by routing uncertain signals for abstention or deferral.
I²RiMA is a novel intra-inter Riemannian manifold attention network for EEG-based mental stress detection. It constructs frequency-specific spatial covariance and uses temporal attention to improve cross-subject stress classification, achieving up to 82.78% balanced accuracy.
This paper introduces a retrieval-augmented personalization method for wearable stress detection using frozen foundation models, achieving near-supervised fine-tuning performance without requiring labeled user data.
This study investigates machine learning models to predict exam outcomes using physiological data such as electrodermal activity, heart rate, and skin temperature, finding that both deep learning approaches and simpler models like random forests can be effective.