Intelligent Yoga Coaching System Based on Posture Recognition

2021 International Conference on Culture-oriented Science & Technology (ICCST) Pub Date : 2021-11-01 DOI:10.1109/ICCST53801.2021.00068

Xiangjie Huang, Da Pan, Yiming Huang, Junli Deng, Pengyu Zhu, Ping Shi, Ruisi Xu, Zelu Qi, Jingqian He

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引用次数: 3

Abstract

Nowadays, yoga as a popular sport has received more and more people’s attention. However, many people do not have enough time and suitable places to practice yoga. In recent years, the development of posture estimation methods has provided convenience for intelligent yoga coaches. This research aims to develop a yoga training system based on real-time pose estimation. The system is composed of a posture recognition network, a yoga standard movement posture library, a yoga movement correction algorithm and a system UI interface. Taking the real-time human motion video stream collected by the camera as input, the gesture recognition network based on OpenPose is used to extract the joint points of human gestures. According to our proposed correction algorithm based on entropy weight, real-time feedback and correction suggestions can be obtained. Experimental results show that this method can show high real-time performance on home computers and high accuracy on real data sets.

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基于姿态识别的智能瑜伽教练系统

如今，瑜伽作为一项流行的运动已经受到越来越多的人的关注。然而，很多人没有足够的时间和合适的场所练习瑜伽。近年来，姿态估计方法的发展为智能瑜伽教练提供了方便。本研究旨在开发一个基于实时姿态估计的瑜伽训练系统。该系统由姿势识别网络、瑜伽标准动作姿势库、瑜伽动作校正算法和系统UI界面组成。以摄像机采集的实时人体运动视频流为输入，利用基于OpenPose的手势识别网络提取人体手势的结合点。根据我们提出的基于熵权的校正算法，可以得到实时反馈和校正建议。实验结果表明，该方法在家用计算机上具有较高的实时性，在实际数据集上具有较高的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2021 International Conference on Culture-oriented Science & Technology (ICCST)

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