Detecting Anxiety and Depression using facial emotion recognition and speech emotion recognition. Written in pythonPython

Facial Expression Recognition (FER) for Mental Health Detection applies AI models like Swin Transformer, CNN, and ViT for detecting emotions linked to anxiety, depression, PTSD, and OCD. It focuses on AI for mental health, emotion detection using OpenCV Python, and real-time applications in healthcare and HR systems.

Code for Anxiety Level Detection from physiological signals using supervised ML algorithms

Depression and anxiety detection on social media with ML - first place in UTD's 2024 AI workshop

COS 429 final project completed by Adam Fisch and Max Shatkhin. Using image processing techniques including Eulerian Video Magnification (see http://people.csail.mit.edu/mrub/vidmag/) and HSV filtering, as well as prediction algorithms such as an HMM, we try to detect anxiety levels of subjects in videos.

Anxiety detection using Deep learning

A deep learning project that predicts emotions from multiple data types — text, audio, and images. It uses CNNs for visual cues, RNNs for analyzing voice tone through MFCCs, and TF-IDF with machine learning for text emotion detection, helping identify stress, anxiety, or calm moods.

MADNet for anxiety detection

Repository for CS224S project: Detecting anxiety from short clips of free-form speech

Machine learning models to detect anxiety and depression through text.

Text analysis for anxiety level detection

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Automatic Detection of Social Anxiety Symptoms using Speech and Facial Recognition

A simple machine learning model using XGBoost and SkLearn for the detection of Parkinsons based on the parkinsons.data dataset. Parkinson's disease (PD), or simply Parkinson's is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms usually emerge slowly and, as the disease worsens, non-motor symptoms become more common.The most obvious early symptoms are tremor, rigidity, slowness of movement, and difficulty with walking.Cognitive and behavioral problems may also occur with depression, anxiety, and apathy occurring in many people with PD.Parkinson's disease dementia becomes common in the advanced stages of the disease.

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This project leverages deep learning to predict depression and anxiety using healthcare data, helping to identify at-risk individuals early. By applying machine learning models, the goal is to improve mental health intervention through accurate and efficient predictions.

Comprehensive mental health support platform with features like a chatbot for early anxiety and depression detection, personal guidance, feedback, and professional login functionality. Built using React, Firebase, and Groq API with enhanced CSS and JavaScript.

Detection of Anxiety using GSR and Heart Rate signals from a hardware device

This repository hosts an AI-supported platform for monitoring and assessing depression and anxiety during the perinatal period. Using predictive analytics, NLP, and wearable data integration, it enables early detection, personalized insights, and secure data handling to enhance maternal mental health care.

This repository provides the code for the paper "Temporal Word Embeddings for Early Detection of Psychological Disorders on Social Media". The project explores how temporal shifts in word usage patterns can be leveraged to identify early warning signs of psychological conditions such as depression and anxiety.

An AI‑powered mental health monitoring system for early detection and safe support of stress, anxiety, and depression. It integrates emotion analysis, crisis screening, PHQ‑2/GAD‑2 assessments, and a Mental Health Index, using fine‑tuned NLP and RAG‑based CBT guidance to deliver personalized, context‑aware support.

This repository contains the hardware design files for a wristband that is designed to detect signs of acute stress and anxiety in real time. The device integrates motion sensing, photoplethysmography (PPG) for heart rate and pulse detection, and electrodermal activity (EDA) monitoring to capture a comprehensive physiological profile of the user.

This project uses machine learning to identify anxiety levels from user data, like text, speech, or physiological signals. It helps early detection and intervention, promoting mental wellness. The system analyzes patterns and provides insights to users or healthcare professionals. It aims to make mental health monitoring accessible and easy.

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A Shiny web application built in R that predicts whether a person is experiencing anxiety based on user inputs. It uses SMOTE for data balancing, LASSO for feature selection, and XGBoost for accurate prediction.

Social Anxiety Detection using ML

Accurately detects anxiety syndromes in patients

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