Pranayama (breathing exercises) is an integral part of yoga practise. When practising pranayama, it's important to keep track of how many cycles you've done. The most important thing is to keep track of how long someone is inhaling and exhaling. Maintaining a proper ratio throughout the inhalation: exhalation cycle is critical. The counting procedure is so taxing for a beginning that it is difficult to retain awareness of the breathing process, and the standard of pranayama practise suffers as a result. The goal of the proposed system is to develop a new method for analysing the quality of Pranayama using machine learning techniques. The main goal of the proposed project is to develop an application that can count inhalations and exhalations. It guarantees that users receive feedback based on their breathing and exhalation patterns. It analyses each breath and exhalation pattern and uses Clustering algorithms to classify inhalation and exhalation. This paper's proposed structure aids in improving the uniformity of pranayama. As a result, respiratory performance improves, which reduces melancholy and anxiety. To test the validity of breathing patterns, the KNN, SVM, Random Forest, and Decision Tree algorithms are used.
Author(S) Details
A. Parkavi
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.
V. Sangeetha
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.
G. R. Amith
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.
B. K. Harini
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.
M. Supriya
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.
K. N. Tejasvini
Department of CSE, M S Ramaiah Institute of Technology, Bangalore, India.
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