Upadhyay Aman, Basha Niha Kamal, Ananthakrishnan Balasundaram
School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore 632014, India.
School of Computer Science and Engineering, Center for Cyber Physical Systems, Vellore Institute of Technology (VIT), Chennai 600127, India.
Healthcare (Basel). 2023 Feb 17;11(4):609. doi: 10.3390/healthcare11040609.
In today's digital world, and in light of the growing pandemic, many yoga instructors opt to teach online. However, even after learning or being trained by the best sources available, such as videos, blogs, journals, or essays, there is no live tracking available to the user to see if he or she is holding poses appropriately, which can lead to body posture issues and health issues later in life. Existing technology can assist in this regard; however, beginner-level yoga practitioners have no means of knowing whether their position is good or poor without the instructor's help. As a result, the automatic assessment of yoga postures is proposed for yoga posture recognition, which can alert practitioners by using the Y_PN-MSSD model, in which Pose-Net and Mobile-Net SSD (together named as TFlite Movenet) play a major role. The Pose-Net layer takes care of the feature point detection, while the mobile-net SSD layer performs human detection in each frame. The model is categorized into three stages. Initially, there is the data collection/preparation stage, where the yoga postures are captured from four users as well as an open-source dataset with seven yoga poses. Then, by using these collected data, the model undergoes training where the feature extraction takes place by connecting key points of the human body. Finally, the yoga posture is recognized and the model assists the user through yoga poses by live-tracking them, as well as correcting them on the fly with 99.88% accuracy. Comparatively, this model outperforms the performance of the Pose-Net CNN model. As a result, the model can be used as a starting point for creating a system that will help humans practice yoga with the help of a clever, inexpensive, and impressive virtual yoga trainer.
在当今的数字世界中，鉴于疫情的不断蔓延，许多瑜伽教练选择在线授课。然而，即使通过学习或接受来自最佳可用资源（如视频、博客、期刊或文章）的培训，用户也无法实时跟踪自己是否正确保持姿势，这可能会在日后导致身体姿势问题和健康问题。现有技术在这方面可以提供帮助；然而，没有教练的帮助，初级瑜伽练习者无法知道自己的姿势是好是坏。因此，本文提出了一种用于瑜伽姿势识别的自动评估方法，该方法可以通过Y_PN-MSSD模型提醒练习者，其中Pose-Net和Mobile-Net SSD（合称为TFlite Movenet）发挥着主要作用。Pose-Net层负责特征点检测，而Mobile-Net SSD层在每一帧中执行人检测。该模型分为三个阶段。首先是数据收集/准备阶段，在此阶段从四名用户以及一个包含七种瑜伽姿势的开源数据集中捕获瑜伽姿势。然后，利用这些收集到的数据对模型进行训练，通过连接人体关键点进行特征提取。最后，识别瑜伽姿势，该模型通过实时跟踪瑜伽姿势来帮助用户，并以99.88%的准确率即时纠正姿势。相比之下，该模型的性能优于Pose-Net CNN模型。因此，该模型可以作为创建一个系统的起点，该系统将借助一个智能、廉价且令人印象深刻的虚拟瑜伽教练来帮助人们练习瑜伽。
