How To Find Yoga Tree Pose Online

But instead of feeling annoyed, frustrated, or disappointed in my inability to find my balance, I laughed as if I was a child again engaged in a game, not caring about the outcome, simply enjoying the process of tree-climbing and falling and climbing again. While, performing Vriksasana it gives the true feeling of Tree standing. All true keypoints of individual body joints are manually added and annotated on individual yoga poses. Table 1 represents the summary of the Yoga Poses Dataset. Each image has a resolution of 300 × 300. The images in the dataset have different backgrounds with various skin colors and hair tones. As shown in Fig. 3, the BlazePose model efficiently detects 33 human body joint landmarks in static images or videos, including the head, torso, arms, and legs. Firstly, detection of human body joints using the BlazePose model for all five yoga poses (for understanding, the general approach is exemplified with one pose goddess in Fig. 2). Secondly, accuracy analysis for all five yoga poses.

The sample images of all mentioned yoga poses are demonstrated in Fig. 1 Below. Therefore, real-time images or video frames collected in CSV file is considered the dataset. Any neural network’s input source is considered an image or video frame in detecting the keypoints to estimate the human pose. BlazePose model is an example of a machine learning pipeline (ML) approach for human pose tracking. The BlazePose model can estimate and detect maximum body joints; it is used for dance poses and fitness applications. Trejo and Yuan proposed recognition of yoga poses using Kinect (Trejo & Yuan, 2018). Kinect technology comprises a depth camera sensor (Shotton et al., 2012). The proposed interactive system recognizes six different yoga poses, with the accuracy of the pose being measured using the mean and deviation of the joint location of concerned body parts. In literature, different machine learning frameworks were adopted to detect body joints, including wearable sensor-based models (Wu et al., 2019; Puranik, Kanthi & Nayak, 2021), a Kinect model (Trejo & Yuan, 2018; Islam et al., 2017), OpenPose model (Cao et al., 2018), and computer vision-based models. Therefore, the sensor-based system is not convenient for users and is impractical.

Lin et al. (2021) introduced a self-practice yoga system for tracking the performance of body posture during exercises. Yamao & Kubota (2021) proposed a human pose recognition system using the PoseNet (Chen et al., 2017) model on the Pi (2015) Platform. The skeleton model represents body-joint localization in the proposed approach (Desai & Mewada, 2021). The methodology to estimate human pose consists of the traditional method (Yang & Ramanan, 2012) and the deep neural network-based approach. Body joints are achieved using the OpenPose (Cao et al., 2018) approach, and 24 body joints are used to represent the complete human skeleton. The proposed approach objective is to detect the number of human body joints and analyze the human pose estimation accuracy using percentage of correct keypoints (PCK) and percentage of detected joints (PDJ) evaluation parameters. In contrast to the conventional pose estimation models, which use 18 body joints to represent a human pose, detecting 33 key points is proposed to achieve higher accuracy. Generally, human body joint detection is carried out using COCO topology, which uses 18 body joints to detect human pose (Lin et al., 2014). Representation of the viewpoint of human body joints as key points is carried out in the form of two different categories: (1) Person Centric (PC) and (2) Observer Centric (OC).

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. As shown in Fig. 4(A), the pose detector model can detect the human pose as the Region-of-Interest (RoI) from the static image or consisting of a video frame. As shown in Fig. 4(B), the pose detector model only runs for the first frame and derives RoI for the video frame data. Virtual motion and pose from images and video can be estimated by detecting body joints and their interconnection. The images or video frame information is often represented in pixel values demonstrated in CSV files. The images are also captured from different camera angles and different lighting conditions. However, the wearable sensor needs the attachment of the sensors to the body joints during yoga, and Kinect based approach needs a depth camera. However, a depth camera sensor is not readily available for users.

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