论文标题
实时识别瑜伽姿势使用计算机视觉进行智能医疗保健
Real-time Recognition of Yoga Poses using computer Vision for Smart Health Care
论文作者
论文摘要
如今，瑜伽已经成为许多人生活的一部分。瑜伽姿势识别实施了练习和运动技术帮助。在这项工作中，开发了一种基于自我助理的瑜伽姿势识别技术，该技术可帮助用户实时执行具有校正功能的瑜伽。这项工作还介绍了瑜伽手Mudra（手势）识别。已经开发了瑜伽士数据集，其中包括10个瑜伽姿势，每个姿势的图像约为400-900张，还包含5个用于识别Mudras姿势的Mudras。它包含大约500张Mudra的图像。该特征是通过在体内制作瑜伽姿势的骨骼并为Mudra姿势的手提取的。两种不同的算法已用于为瑜伽姿势创建骨架，第二种用于手工泥。关节的角度已被提取为不同机器学习和深度学习模型的功能。在所有模型中，XGBoost带有随机搜索简历最准确，并且具有99.2 \％的精度。完整的设计框架在本文中描述。
Nowadays, yoga has become a part of life for many people. Exercises and sports technological assistance is implemented in yoga pose identification. In this work, a self-assistance based yoga posture identification technique is developed, which helps users to perform Yoga with the correction feature in Real-time. The work also presents Yoga-hand mudra (hand gestures) identification. The YOGI dataset has been developed which include 10 Yoga postures with around 400-900 images of each pose and also contain 5 mudras for identification of mudras postures. It contains around 500 images of each mudra. The feature has been extracted by making a skeleton on the body for yoga poses and hand for mudra poses. Two different algorithms have been used for creating a skeleton one for yoga poses and the second for hand mudras. Angles of the joints have been extracted as a features for different machine learning and deep learning models. among all the models XGBoost with RandomSearch CV is most accurate and gives 99.2\% accuracy. The complete design framework is described in the present paper.
