Yoga Pose Detection and Classification using Deep Learning
Yoga Posture Recognition and Correction project aims to develop a deep learning-based real-time pose detection and correction system using computer vision techniques. By analyzing body postures and joint angles, the system identifies deviations from ideal poses and provides corrective feedback to help users adjust their positions in real time. This solution leverages MediaPipe Pose for body landmark detection and a neural network model for classifying postural adjustments. Each pose is labeled with correction instructions (‘OK’, ‘Move Up’, ‘Move Down’, ‘Move Left’, and ‘Move Right’) based on the direction needed to adjust the posture to the ideal position.
Yoga Pose Detection using Machine Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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