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An algorithm is designed to measure PCK and PDJ in which the distance between the predicted joint location and true joint location is calculated. Estimated body joints are accurate if the location of estimated body joints is the same as actual joints. Body joints are achieved using the OpenPose (Cao et al., 2018) approach, Yoga Tree Pose and 24 body joints are used to represent the complete human skeleton. A detailed analysis of the body joint detection accuracy is proposed in the form of percentage of corrected keypoints (PCK) and percentage of detected joints (PDJ) for individual body parts and individual body joints, respectively. This article presents an in-depth analysis of each detected keypoint and a comprehensive study on various yoga poses using these key points. Application estimates the keypoints and calculates the score by comparing estimated key points with corrected keypoints. The application is built based on the OpenPose model to detect correct body key points during yoga.

The application also uses vocal instruction to assist yoga students in providing a cooperative environment. To guide and supervise yoga students for their correct yoga poses, Huang et al. Another alternative to assess the yoga pose classification is based on joint detection accuracy parameters viz. Thar, Winn & Funabiki (2019) proposed yoga pose classification methods for self-learning. Wu et al. (2019) presented artificial intelligence models using these sensor data for yoga pose recognition. The presented approach does not measure the accuracy of body-joint localization as per the standard evaluation parameters of body joints. The mean and standard deviation of the measured angle is classified in the posture evaluation in three different classes. In the later stage of the proposed approach, the pose estimation accuracy is measured in the form of standard evaluation parameters, viz. The proposed approach is more inclined to the accuracy of body posture rather than body-joint localization. According to the authors’ knowledge, quantitative evaluation of body-joint detection is unique among all literature. The experiment evaluation suggests that the adopted model obtained 93.9% PCK for the goddess pose. The recent trend is to follow the yoga pose online or through recorded videos. We do 30 minutes of spin and 30 minutes of yoga.

Therefore, accurate localization of these body joints is of utmost importance for estimating the various yoga poses. Furthermore, 33 body joints were identified from the BlazePose model representing the whole body. However, travelling can take a toll on your mind and body, and taking long flights indeed becomes gruelling for the body. 4:30 - 5:30 PM: have a phone meeting with my editor/ publisher to go over details of my book, Yogalosophy: The ULTIMATE 28-Day Mind Body Makeover (which comes out in March) and the accompanying photoshoot. Whwn its comes to pranayama nad meditation the soham yoga school hold the top place because of it expertise in pranayama and meditation as its its has most renowed yoga teacher in world for breatg work and meditation. Inhale, lift up, hold the breath in the pose for a few seconds and come back to the initial position with an exhale (Repeat 10 times). Come to seated on the carpet and open your legs a comfortable wide distance apart, where you can still sit tall from the sit bones to the crown of your head (feel free to put a folded blanket under your hips) and feel a stretch in your inner legs and hips.

Start with feet hip-width apart, and your arms by the sides of your body.- Interlock your fingers, turn the palms away. This will help you align your hips and to get a solid basis when lifting your arms. If done with proper engagement of muscles it can help you to decompress the joints in the body. However, the wearable sensor needs the attachment of the sensors to the body joints during yoga, and Kinect based approach needs a depth camera. Accurate body joint detection plays a primary role in pose estimation and classification. The existing yoga pose identification techniques focus more on deep neural network-based classification models, and most literature evaluated the classification rate using confusion matrix parameters. Various deep neural networks like DeepPose (Toshev & Szegedy, 2014), OpenPose (Cao et al., 2018), Convolutional Pose Machine (Wei et al., 2016), Stacked Hourglass Network (Newell, Yang & Deng, 2016), BlazePose (Bazarevsky et al., 2020), etc., are used to identify accurate body joint localization on the human pose. If the localization of joints is in 2D coordinates, then the estimated pose is considered 2D human pose estimation; otherwise, it is deemed to be 3D human pose estimation (Zheng et al., 2020). Representation of detected body joint localization on the input data consists of three approaches: (1) skeleton model, where body joints are detected in the point form and connected in the form of a line that creates limbs.