Mental Health

A comprehensive review on AI-powered wearable biosensors, highlighting how machine learning and edge AI enable real-time health monitoring and personalized care, including digital twins, LLMs, and challenges in privacy, scalability, and clinical integration.

The CAN-STRESS dataset provides multimodal physiological and self-reported data from 82 participants (39 cannabis users and 43 non-users) collected in real-world conditions using Empatica E4 wristbands. Preliminary analysis shows machine learning models can distinguish users from non-users with high accuracy, with electrodermal activity and heart rate emerging as key predictors.

This paper introduces a multimodal blood glucose forecasting framework that combines time-aware cross-attention with an LSTM to predict glucose levels from CG) data and complementary wearable signals (heart rate, EDA, accelerometry, and diet).

CUDLE is a self-supervised learning framework that improves cannabis use detection from wearable sensor data in real-world settings, outperforming supervised methods while requiring significantly fewer labeled samples.

Chronic cannabis users show disrupted diurnal cortisol rhythms, including a blunted cortisol awakening response and elevated afternoon cortisol levels, but no major differences in diurnal heart rate variability or electrodermal activity, except for increased evening heart rate. Acute cannabis use reduced cortisol, subjective stress, and electrodermal activity. These findings suggest dysregulated stress responses in cannabis users, potentially linked to later waking times and cannabis’s stress-relieving effects, warranting further research on the relationship between cannabis use, cortisol rhythms, and psychological disorders.

We present a data-driven approach for stress detection based on convolutional neural networks while addressing the problems of the best sensor channel and the lack of knowledge about stress episodes.

A multi-modal dataset for stress and alcohol relapse collected in real world settings.

A stress classification and personalization framework based on Convolutional Neural Networks.