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Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.

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FPT-ThaiTuan/Detect-Yoga-Poses-And-Correction-In-Real-Time-Using-Machine-Learning-Algorithms

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DATASET
DATASET
 
 
teacher_yoga
teacher_yoga
 
 
README.md
README.md
 
 
demo.py
demo.py
 
 
downdog_warrior2.mp4
downdog_warrior2.mp4
 
 
give_angle_teacher.py
give_angle_teacher.py
 
 
main.py
main.py
 
 
test_angle.csv
test_angle.csv
 
 
train_angle.csv
train_angle.csv
 
 
utils.py
utils.py
 
 

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1. The problem consists of two main parts

Part 1: Classifying movements based on SVM (Support vector machines)

Part 2: Posture assessment gives real-time feedback

2. Collect data

Collected from kaggle : YOGA_POSE_DATASET

3. Result

Classification results with test data

Screenshot 2023-12-22 000624

Confusion matrix

Screenshot 2023-12-21 234832

Results of yoga posture correction method

Screenshot 2023-12-21 234941

Hope this article can help you.

If you have any questions please contact me for help!

Gmail: tuanddt.ai.work@gmail.com

Thanks everyone!

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Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements.

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machine-learning computer-vision deep-learning support-vector-machines pose-estimation long-short-term-memory svc-model lsmt blazepose yoga-classfication

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