Towards Robust and Efficient Yoga Pose Recognition using Deep Learning: A Comprehensive Review.

Yoga is a popular practice that promotes physical and mental well-being. The lack of regulation and standardization in yoga teaching and learning can lead to unsafe practices and health issues for practitioners, such as injury or exacerbation of pre-existing medical conditions. Accurate classification of yoga postures is essential for effective teaching and learning of yoga. Deep Learning (DL) techniques have shown significant potential in automating the process of posture classification, leading to improve accuracy and efficiency. This paper provides a comprehensive review of the use of DL techniques for yoga posture classification. Different DL algorithms, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Generative Adversarial Network are explored. Additionally, various datasets used for training and testing deep learning models for yoga posture classification are discussed. Challenges involved in yoga posture classification, such as variability in posture performance and the lack of standardized datasets, are also highlighted. A comparison of different DL techniques for yoga posture classification is presented, mentioning their respective strengths and weaknesses. This review emphasizes the potential benefits of DL for accurate yoga posture classification and provides insights into future research directions in this field. [ABSTRACT FROM AUTHOR]