Mental Health

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.