# Deep Learning Models for Yoga Pose Monitoring
## 1 Introduction
This field addresses the use of different architectures based on artificial neural networks that, through a hierarchy of layers with non-linear processing units, learn high-level abstractions for data. Machine learning as well as deep learning techniques have been extensively employed to explore the causal relations and correlations among big data. One of the key objects of research in the scientific domains of deep learning and computer vision is the human skill to identify and interpret another person's actions. Activity recognition refers to the vast topic of study that focuses on the detection of the activities and goals of one or more individuals from a sequence of observations of the individuals' actions and surroundings. Human activity recognition is the subset concerning the modeling of the human body to recognize a person's unique motion and behavior through data received from either sensors or vision-based techniques. The movements to be identified may include common activities such as sitting, standing, conversing, or walking, or may encompass highly specific and complicated gestures such as the vivid yoga postures, or specific athletic stances [9]. Accurate classification of human poses still poses a challenge to the scientific community, stimulating further work in this area. Across the various characterization methods prevalent, a two-fold approach to the categorization problem remains uniform: firstly, the recognition part, and secondly, the localization problem, which indicates the description of the action to be detected and the identification of the portion of real-time movements that contains the object of interest respectively [1; 10]. The entire process generally consists of three stages in terms of representation: the low-level core technology, the mid-level behavior detection, and the high-level implementation [2]. In the initial step of the first phase, object segmentation is conducted on each frame of the video to distinguish and isolate the target object from its environment. The second level involves the retrieval of the attributes of separated objects such as color, shape, outlines, and movements and their subsequent representation into features, which can be broadly classified into four main types: frequency transform, local identifiers, space-time statistics, and body modeling data. The event detection and classification techniques employed to detect various human poses and movements predicated on the features derived from the previous step comprise the third stage of the operation. It concerns the difficulties faced at each of these sub-levels in the wake of a multitude of factors such as poor illumination, changes in video quality, partial visibility, closeness, background disturbance, angle involved, etc., that make human activity recognition a rather arduous task [11]. Intensive studies continue to be conducted within this field, with an aim to improve upon the existing results achieved. Lately, methods based on deep learning have gained popularity due to their use of representation learning algorithms that can instinctively extract suitable features from the input data supplied by sensors without the need for human interference and unique underlying patterns [12; 13]. In this particular paper, we propose a deep learning model for recognizing and correcting yoga poses in real-time, with the motive to address the lack of availability of proper training and to make such education more affordable and suitable for people. This is implemented by attempting to exploit the opportunity of self at-home training through an application that can accurately differentiate between the poses practiced by expert trainers from those of beginners, resulting in reliable recommendations to learners concerning necessary corrections.