Yoga has become increasingly popular in recent years due to its many physical and mental health benefits. With the advent of technology-based tools, there has been a growing interest in developing systems to enhance the practice of yoga. One such system is a yoga pose detection and feedback generation system, which can provide practitioners with feedback on their form and technique. In this survey research paper, we provide a comprehensive analysis of the current state-of-the-art in yoga pose detection and feedback generation by reviewing 52 recent research papers on the topic.

Our study found that various techniques have been proposed for pose recognition, including deep learning-based methods such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Other techniques include skeleton-based methods, which use pose estimation algorithms, and pose graph-based methods, which use a graph-based representation of poses. The choice of technique depends on factors such as the level of accuracy required, the available data, and the computational resources available.

Feedback generation techniques are often used in combination with pose recognition techniques. Feedback generation methods include virtual assistants, smart yoga mats, and haptic feedback devices. These techniques provide feedback on posture, alignment, and breathing to help practitioners improve their form and technique.

In conclusion, the development of yoga pose detection and feedback generation systems using deep learning techniques has shown promise in improving the practice of yoga. The combination of pose recognition and feedback generation techniques can help practitioners achieve correct posture, alignment, and breathing, leading to a more effective and safer practice of yoga. Further research is needed to explore the potential of these systems for different types of yoga and for different levels of practitioners.