Deep Learning-Based Yoga Posture Specification Using OpenCV and Media Pipe

Abstract

Yoga is a year’s discipline that calls for physical postures, mental focus, and deep breathing. Yoga practice can enhance stamina, power, serenity, flexibility, and well‐being. Yoga is currently a well-liked type of exercise worldwide. The foundation of yoga is good posture. Even though yoga offers many health advantages, poor posture can lead to issues including muscle sprains and pains. People have become more interested in working online than in person during the last few years. People who are accustomed to internet life and find it difficult to find the time to visit yoga studios benefit from our strategy. Using the web cameras in our system, the model categorizes the yoga poses, and the image is used as input. However, the media pipe library first skeletonizes that image. Utilizing a variety of deep learning models, the input obtained from the yoga postures is improved to improve the asana. On non-skeleton photos, VGG16, InceptionV3, NASNetMobile, YogaConvo2d, and also InceptionResNetV2 came in the order of highest validation accuracy. The proposed model YogaConvo2d with skeletal pictures, which is followed by VGG16, reports validation accuracy in contrast, NASNetMobile, InceptionV3, and InceptionResNetV2.