Our project is an IoT-enabled system that tracks both physiological (e.g., heart rate variability, sleep patterns) and environmental (e.g., noise, light) factors to assess mental health. The solution offers personalized mental wellness recommendations, detects signs of stress or anxiety, and connects users to mental health professionals when needed. This user-friendly Android application integrates various sensors, APIs, and data analysis tools to help users keep track of their mental health.
- User-Friendly Interface: An intuitive and easy-to-navigate interface for seamless user interaction.
- Integrated Chatbot: Assists users with mental health queries and provides support.
- Sleep Cycle Analysis: Monitors sleep patterns using screen time data.
- Speech-to-Text: Converts spoken words into text for easier analysis.
- Sentiment Analysis: Analyzes text data to determine emotional states.
- Heart Rate Monitoring: Uses the phone camera to monitor heart rate.
- Background Noise Analysis: Utilizes the phone's microphone to analyze ambient noise levels.
- Symptom Detection: Employs image analysis to detect physical symptoms.
- Anxiety-Reducing Games: Includes games designed to alleviate anxiety.
- Meditation and Breathing Exercises: Provides exercises to help users relax and manage stress.
- Smart Watch Integration: Collects additional data from connected smart watches.
- Voice Analysis: Analyzes voice recordings to assess mental health.
- APIs: Google Fit, Android Sleep, and others for data collection.
- Data Analysis: Matplotlib, Seaborn, and other modules for analyzing collected data.
- Sensors: Various mobile phone sensors for comprehensive health monitoring.
Our project aims to provide an all-encompassing mental health monitoring system that leverages modern technology to deliver accurate and actionable insights into users' mental well-being.