The Posture Detection project is a machine learning-based system designed to detect and classify human posture in real-time using video input. Utilizing various algorithms and real-time processing, this project aims to assist users in maintaining proper posture, especially during prolonged periods of sitting.

• Real-Time Posture Detection: Leverages a webcam to monitor the user's posture continuously.
• Pose Estimation: Utilizes MediaPipe's Pose solution for accurate pose landmark detection.
• Posture Classification: Employs a machine learning model to classify the posture as 'Good' or 'Needs Adjustment'.
• Feedback System: Provides real-time visual feedback on the user's current posture.

• ML_Pipeline.py: Sets up the machine learning pipeline, including data preprocessing, model training (using various algorithms), and evaluation.
• Posture Detection.py: The main script for real-time posture monitoring, leveraging the webcam and ML model to provide immediate posture assessment.
• captureData.py: Similar to the main script but potentially used for data collection or additional testing purposes.

To set up the project, follow these steps:

• Clone the repository.
• Install the required dependencies:

pip install -r requirements.txt

Run Posture Detection.py to start the posture detection in real-time.

Ensure your webcam is enabled and properly positioned. Execute Posture Detection.py to start monitoring. The system will display your current posture status on the screen.

• Python
• OpenCV (cv2)
• MediaPipe
• Scikit-Learn
• NumPy