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Yoga posture recognition remains a difficult issue because of crowded backgrounds, varied settings, occlusions, viewpoint alterations, and camera motions, despite recent promising advances in deep learning. In this paper, the method for accurately detecting various yoga poses using DL (Deep Learning) algorithms is provided. Using a standard RGB camera, six yoga poses — Sukhasana, Kakasana, Naukasana, Dhanurasana, Tadasana, and Vrikshasana — were captured on ten people, five men and five women. In this study, a brand-new DL model is presented for representing the spatio-temporal (ST) variation of skeleton-based yoga poses in movies. It is advised to use a variety of representation learners to pry video-level temporal recordings, which combine spatio-temporal sampling with long-range time mastering to produce a successful and effective training approach. A novel feature extraction method using Open Pose is described, together with a DenceBi-directional LSTM network to represent spatial-temporal links in both the forward and backward directions. This will increase the efficacy and consistency of modeling long-range action detection. To improve temporal pattern modeling capability, they are stacked and combined with dense skip connections. To improve performance, two modalities from look and motion are fused with a fusion module and compared to other deep learning models are LSTMs including LSTM, Bi-LSTM, Res-LSTM, and Res-BiLSTM. Studies on real-time datasets of yoga poses show that the suggested DenseBi-LSTM model performs better and yields better results than state-of-the-art techniques for yoga pose detection. [ABSTRACT FROM AUTHOR]

Artificial Intellegence is an assemblage of many algorithms for analysing and interpreting knowledge from vast collection of heterogenous data, which influenced a wide range of indusries. The concepts of AI are related to fields like statistics, probability, pattern recognition, machie learning etc. collectively called as “computational intelligence”. This paper analyses the impact of these techinques in the healthcare life cycle starting from diagnosis to treatment and also its contribution in prevention. As a small error in the applicability will lead to dangeous and ireversible effect, this paper analyses the means by which governments taking care of and ensuring their performance while giving permission for the products based on AI in healthcare. [ABSTRACT FROM AUTHOR]