Anxiety Detection

Anxiety detection research aims to develop accurate and efficient methods for identifying and assessing anxiety levels, primarily to improve mental health diagnosis and treatment. Current research heavily utilizes machine learning, particularly deep learning models like convolutional and recurrent neural networks, along with support vector machines and random forests, analyzing diverse data sources including EEG signals, speech patterns, text from social media, and physiological measures like heart rate. These efforts are significant because they could lead to more objective, accessible, and timely anxiety assessments, potentially improving early intervention and personalized treatment strategies.