![]() Human activity recognition with openpose and Long Short-Term Memory on real time images. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, 85–93. Hockey action recognition via integrated stacked hourglass network. 3D Human pose estimation using convolutional neural networks with 2D pose information. Forecasting human dynamics from static images (pp. (2020) “Infinity yoga tutor: Yoga posture detection and correction system.” 2020 5th International Conference on Information Technology Research (ICITR) (pp. Rishan, F., de Silva, B., Alawathugoda, S., Nijabdeen, S., Rupasinghe, L., & Liyanapathirana, C. ![]() IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 1302–1310. Realtime multi-person 2D pose estimation using part affinity fields. Ĭao, Z., Simon, T., Wei, S., & Sheikh, Y. (2020) “Robust vision-based workout analysis using diversified deep latent variable model,” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. “Pose estimation and action recognition in sports and fitness” Master’s Projects. International Journal of Machine Learning and Cybernetics, 11, 2529–2540. Human posture recognition based on multiple features and rule learning. XNect: Real-time multi-person 3D motion capture with a single RGB camera. Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Elgharib, M., Fua, P., Seidel, H.-P., Rhodin, H., Pons-Moll, G., & Theobalt, C. Match pose-A system for comparing poses, International Journal of Engineering Research & Technology (IJERT), 08(10) (2019, October). Virtual video synthesis for personalized training (pp. Markolefas, F., Moirogiorgou, K., Giakos, G., & Zervakis, M. International Journal of Scientific Research in Computer Science Engineering and Information Technology. Yoga pose detection and classification using deep learning. Real-time yoga recognition using deep learning. Yadav, S., Singh, A., Gupta, A., & Raheja, J. IEEE Engineering in Medicine and Biology Society (pp. ![]() Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Realtime indoor workout analysis using machine learning & Computer vision. Nagarkoti, A., Teotia, R., Mahale, A., & Das, P. “ 7 common exercises you’re doing wrong-and how to fix them.” Openfit, August 29, 2020. “64 per cent Indians don’t exercise: Study-Times of India.” The Times of India, July 3, 2019. With the help of this model, fitness enthusiasts can perform a particular workout accurately at the comfort of their home without getting injured and with proper guidance. The maximum score that the PoseNet model achieves ranges from 0.92874 to 0.98325 for all the key points. This model has been achieved using the PoseNet library on Tensorflow. After synchronizing user and reference image or video the system gives a green skeleton if the user posture is correct and a red skeleton if the user posture is incorrect. The system will analyze the angles between the limbs of the body and compare it to the reference video or image using the Cosine rule. Hence, this application BeFit is proposed that analyzes the posture of the user performing a particular workout by comparing their workout to the reference image or video provided by the system. However, incorrect posture during exercises may lead to severe long-term injuries such as back pain, Tendinitis or even hamstring strains. Exercising regularly is very important as it helps improve the quality of life. ![]() Maintaining one’s physical fitness is of utmost importance.
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