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Infinity Yoga Tutor: Yoga Posture Detection and Correction System

机译：无限瑜伽导师：瑜伽姿势检测和校正系统

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Popularity of yoga is increasing daily. The reason for this is the physical, mental and spiritual benefits that could be obtained by practicing yoga. Many are following this trend and practicing yoga without the training of an expert practitioner. However, following yoga in an improper way or without a proper guidance will lead to bad health issues such as strokes, nerve damage etc. So, following proper yoga postures is an important factor to be considered. In this proposed system, the system is able to identify poses performed by the user and also guide the user visually. This process is required to be completed in real-time in order to be more interactive with the user. In this paper, the yoga posture detection was done in a vision-based approach. The Infinity Yoga Tutor application is able to capture user movements using the mobile camera, which is then streamed at a resolution of 1280 × 720 at 30 frames per second to the detection system. The system consists of two main modules, a pose estimation module which uses OpenPose to identify 25 keypoints in the human body, using the BODY_25 dataset, and a pose detection module which consists of a Deep Learning model, that uses time-distributed Convolutional Neural Networks, Long Short Term Memory and SoftMax regression in order to analyze and predict user pose or asana using a sequence of frames. This module was trained to classify 6 different asanas and the selected model which uses OpenPose for pose estimation has an accuracy of 99.91%. Finally, the system notifies the users on their performance visually in the user interface of the Mobile application.
机译：瑜伽的普及日报日益增加。这是通过练习瑜伽来获得的物理，精神和精神效益。许多趋势和练习瑜伽在没有专家从业者的培训的情况下。然而，在不正确的方式或没有适当的指导下瑜伽会导致冲程，神经损伤等的不良健康问题，所以，遵循适当的瑜伽姿势是一个重要的因素。在该提出的系统中，该系统能够识别用户执行的姿势，并在视觉上引导用户。该过程需要实时完成，以便与用户更互动。在本文中，瑜伽姿势检测以基于视觉的方法进行。无限瑜伽导师应用程序能够使用移动摄像机捕获用户移动，然后在每秒30帧的分辨率为每秒到检测系统的分辨率流流。该系统由两个主模块组成，姿势估计模块使用openpose来识别人体中的25个关键点，该模块使用BOST_25数据集和由使用时间分布式卷积神经网络组成的姿势检测模块来识别人体中的25个关键点。 ，长期内记忆和Softmax回归，以便使用一系列帧分析和预测用户姿势或asana。此模块培训以对6种不同的ASAN和所选模型进行分类，并使用Open的所选模型进行姿势估计的精度为99.91％。最后，系统将用户在移动应用程序的用户界面中对用户通知其性能。

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