Analysis of the Impact of Yoga on Health Care Applications and Human Pose Recognition

Abstract

Human Pose Recognition is a powerful computer vision strategy that has revealed several problems. Separating human exercise is beneficial in various fields, including health observation, biometrics, and a wide range of medical care applications. These days, yoga poses are famous for practice because they can improve muscular quality and increase breathing exercises. However, because evaluating yoga stances is complicated, experts will be unable to profit from the activities in the long run. For people who want to practice yoga at home, IoT-based yoga systems are required. Several studies have suggested that camera-based or wearable devices can better arrange yoga pose discovering approaches. On the other hand, camera-based methods have security and protection issues, and wearable device-based strategies have proven irrational in existing applications. A solid foundation and ongoing research in pose assessment are required to construct such a framework. First, using real-time data, this paper investigates the effect of yoga on people experiencing various anxiety levels. Second, a comprehensive survey of yoga pose detection frameworks, ranging from machine learning to deep learning techniques and assessment measurements, was carried out.