View Proposal

Proposer
Drishty Sobnath
Title
Yoga Pose Corrector Using Machine Learning
Goal
This project involves developing a machine learning–based system that can recognize and evaluate yoga poses in real time to provide corrective feedback.
Description
Using pose estimation algorithms (e.g., OpenPose or MediaPipe), the system tracks key body joints from video or camera input and compares them against ideal pose templates. A classification model or rule-based system then determines pose correctness and identifies misalignments. The system provides audio or visual feedback to guide users toward proper alignment, making yoga practice safer and more effective especially for remote or self-guided sessions. Train a custom CNN/LSTM model for temporal pose sequence evaluation. Add real-time feedback via a mobile app. Include difficulty-based pose classification and progress tracker
Resources
Background
Url
Difficulty Level
Moderate
Ethical Approval
None
Number Of Students
0
Supervisor
Drishty Sobnath
Keywords
Degrees
Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Data Science
Master of Science in Software Engineering
Bachelor of Science in Computing Science
BSc Data Sciences
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