Exploring the Foundations of Yoga Philosophy and Its Relevance in Contemporary Society: A Deep Learning-Based Analysis

Abstract

The confluence of traditional Indian knowledge systems with modern practices, especially in the field of yoga, is an intriguing conversation that has an international appeal. This study explores the many facets of Indian knowledge bases and their ongoing development, concentrating on the continued applicability and modernization of yoga. This study aims to shed light on the complex web that unites tradition and innovation through a thorough investigation of historical foundations, philosophical foundations, scientific discoveries, sociocultural ramifications, and commercialization dynamics. We suggest a method for the effective identification and recognition of different yoga poses using deep learning technologies. The Media Pipe library is used to extract the essential points of users from the selected dataset, which consists of 85 films with 15 individuals performing 6 yoga poses. Using real-time observed movies, a deep learning model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has been used to recognize yoga poses. In particular, the CNN layer extracts features from the important places, and the LSTM layer that follows recognizes the presence of a frame sequence so that predictions can be made. The stances are categorized as either right or wrong in the following; if a correct pose is found, the system will give the user the appropriate response via speech or text.