# Deep Learning Models for Yoga Pose Monitoring
## 2 Related Work  
Different methods and techniques for a variety of applications have been proposed and employed in the field of real-time human monitoring. The proposed research aims to take into account the existing literature and further contribute towards an enhancement in the currently achievable results in the field of computer vision for human pose estimation [14]. Furthermore, the effectiveness of LSTM neural network and its important contribution for specific tasks was proved [15; 16]. An attempt has been developed to devise a deep learning based system for efficiently detecting yoga poses and practically works as a substitute for a trainer by giving accurate feedback to the user. A comparative study between machine learning and deep learning techniques for yoga pose estimation was carried out in [3] involving both technologies and analyzed the performance of the framework by using Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Convolutional Neural Network along with Long Short-TermMemory (LSTM). The research concluded that the performance of hybrid CNN-LSTM model resulted in the least number of misclassifications. A system where the skeleton of the user is initially detected using an algorithm called tf-pose estimation, was introduced in [17]. In following, six different machine learning models, namely, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, SVM and KNN were tested and compared. The used dataset contained 10 different yoga poses with around 5500 images and the optimum performance was obtained by the Random Forest classifier with an accuracy of 99.04%. A model named ExNet, which is a multilayer Convolutional Neural Network, was proposed in [18]. The authors used an image dataset containing 2000 images of human exercise poses divided into five classes labeled as push up, pull up, cycling, swiss ball hamsring curl, walking. The model used Adam optimizer and automatic Learning Rate reduction method. After 50 epochs, ExNET got 82.68% accuracy on classifying 2D human exercise pose from the dataset. However, the model needs better hyper-parameter tuning because it faces the problem of overfitting. The authors in [19] managed to accurately design a real-time deep learning model using OpenPose to extract the keypoints from the videos supplied using a dataset comprising of six asanas, i.e., Trikonasana, Padmasana, Vrikshasana, Bhujangasana, Tadasana and Shavasana. While system was capable of an accuracy of 99.04% with the input taken framewise, the detection on the basis of a survey of the preset 45 edges yielded a performance measure of 99.38%. The work was further tested on twelve individuals and proved to be highly effective with an accuracy of 98.92%. The authors in [20] captured video information using Microsoft Kinect. Kinect has a skeletal pursuit tool that may acknowledge twenty joints of a person's body. The chosen ten joint points were used for calculation and the reference structure for each yoga position was engineered by gathering info of the joint points from human posture. In following, the cosine similarity of vectors was calculated by finding angles between all the vectors connecting any two joint points. Specifically, if the calculated deviation is more than a set threshold value, the pose is classified as incorrect. A system capable of detecting poses associated with four kinds of normal human activity and identifying human action in both the second and third dimension, was presented in [21]. The research was conducted on data derived from four different sources, i.e., Human 3.6M, NTU, Penn Action and MPII using a Multitask Convolutional Neural Network [22]. In another employed method, when a pose is made by the user in front of his camera, Mediapipe library implements the geometric analysis based on the frames obtained from the camera [23]. Geometric analysis produces the output based on the angles between various joints. The calculated angles are then compared with the accurate angles that are saved in the database for the specific yoga pose.