A Novel Approach to Analyze Pranayama through Machine Learning Techniques - A. Parkavi; V. Sangeetha; G. R. Amith; B. K. Harini; M. Supriya; K. N. Tejasvini (New Approaches in Engineering Research Vol. 12, 2021)
In yoga practice, pranayama breathing practice is considered as very important. When practicing pranayama, practitioners must note the number of pranayama cycles. Most importantly, the period to which someone is inhaling/exhaling must be maintained properly. It is of great importance and also important to maintain a precise ratio throughout the inhalation: exhalation cycle. For a beginner, the counting process is so demanding that it is difficult to maintain awareness of the breathing process, and it decreases the standard of pranayama practice. The Proposed system is to find a novel approach to analyze the quality of Pranayama using Machine learning Techniques. The main objective of the proposed work is to create an application that is capable of counting the inhalation and exhalation. It ensures that feedback is given to users observing the inhalation and exhalation patterns. It analyses every inhalation and exhalation pattern, and classify inhalation and Exhalation using Clustering techniques. The proposed framework in this paper helps to improve the consistency of pranayama. And thus, enhances breathing performance, which in turn decreases depression and anxiety. Analysis is conducted using the KNN, SVM, Random Forest and Decision tree algorithm to verify the valid breathing patterns.