Research machine learning methods used to detect anxiety using biological signals for students

Abstract

Excessive anxiety disorders affect the ability to function daily. In the school environment, this can include forms such as social anxiety and school anxiety. The main markers of human well-being employed in clinical settings are features taken from ECG, EDA, EEG, and RSP signals. Early discovery and intervention in cases of AD are critical since any mental condition may be improved with early recognition and care. Applying machine learning to these signals enables clinicians to recognize patterns of anxiety and differentiate a sick patient from a healthy one. Further, models with multiple and diverse biosignals have been developed to improve accuracy and convenience. This review introduces several models for detecting anxiety.