Anxiety Monitoring System: A Preliminary Approach.

Nowadays, there is an increasing number of mental illnesses, especially anxiety, being estimated that 284 million people were living with this disorder, in 2018. This study raises awareness of this mental illness and addresses the challenge of stress/anxiety detection using a supervised machine learning system for classification. We give a focus on the respiratory system and its parameters that correlate with stress/anxiety. The developed work establishes the framework for an anxiety monitoring system using multiple physiological parameters. 5 of the most common algorithms were used for the task and the one achieving the best results was the random forest classifier with 92% accuracy and great values for precision, recall, f1-Score and cohen kappa score. Ultimately, this technology can be applied to self and autonomous stress/anxiety detection purposes or partner with specialists who deal with these problems on a day-to-day basis like psychologists or psychiatrists.