An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition - Nguyen Hai Thanh; Truong Nguyen Nhat; Pham Linh Thuy Thi; Pham Ngoc Huynh (Applied Computer Systems)
Amid a rapidly developing era, people can inevitably have problems with stress, depression, pressure, or difficulty sleeping due to frequent overthinking. To overcome the above problems, yoga will be an excellent solution to help adjust thoughts and harmonize body and soul, helping us relax, relax the mind, and retain positive thoughts. Negative and evil auras will be pushed away, and the worldview will improve. Yoga practice has incorrectly caused many unwanted injuries for practitioners. Therefore, we present an approach grounded in skeleton-based feature extraction and neural networks to find a solution to the recognition of yoga postures, creating a premise for researching a smart virtual trainer that supports home workouts for users from input image data converted into skeleton data through MoveNet. The classification models were used to train recognition and classification of yoga poses. The models were trained and evaluated on a dataset of 3939 images of 10 yoga poses. Experimental results show that the proposed algorithms are entirely suitable for the classification task when achieving good results on different metrics such as Precision, Recall, F1-score, and Accuracy.