YogLiMLP: Hybrid LGBM-MLP based classification model for yoga poses

Key Points

• Utilizing a hybrid LGBM-MLP classifier improves the accuracy of yoga pose recognition, indicating significant advancements in this area.
• Model performance metrics highlight a 92% accuracy in classifying various yoga poses, a notable improvement over previous systems.
• Assessment involves data processing techniques such as feature extraction, ensuring the model effectively interprets yoga poses from inputs.
• Enhancing efficiency of yoga pose classification could lead to better tools for fitness and wellness applications.