Long Chhaihuoy, Jo Eunhye, Nam Yunyoung
Department of ICT Convergence, Soonchunhyang University, Asan, 31538 South Korea.
ICT Convergence Research Center, Soonchunhyang University, Asan, 31538 South Korea.
J Supercomput. 2022;78(4):5269-5284. doi: 10.1007/s11227-021-04076-w. Epub 2021 Sep 20.
Yoga is a form of exercise that is beneficial for health, focusing on physical, mental, and spiritual connections. However, practicing yoga and adopting incorrect postures can cause health problems, such as muscle sprains and pain. In this study, we propose the development of a yoga posture coaching system using an interactive display, based on a transfer learning technique. The 14 different yoga postures were collected from an RGB camera, and eight participants were required to perform each yoga posture 10 times. Data augmentation was applied to oversample and prevent over-fitting of the training datasets. Six transfer learning models (TL-VGG16-DA, TL-VGG19-DA, TL-MobileNet-DA, TL-MobileNetV2-DA, TL-InceptionV3-DA, and TL-DenseNet201-DA) were exploited for classification tasks to select the optimal model for the yoga coaching system, based on evaluation metrics. As a result, the TL-MobileNet-DA model was selected as the optimal model, showing an overall accuracy of 98.43%, sensitivity of 98.30%, specificity of 99.88%, and Matthews correlation coefficient of 0.9831. The study presented a yoga posture coaching system that recognized the yoga posture movement of users, in real time, according to the selected yoga posture guidance and can coach them to avoid incorrect postures.
瑜伽是一种有益健康的运动形式，专注于身体、心理和精神的联系。然而，练习瑜伽时采用不正确的姿势可能会导致健康问题，如肌肉扭伤和疼痛。在本研究中，我们提出基于迁移学习技术，利用交互式显示器开发一种瑜伽姿势指导系统。从RGB相机收集了14种不同的瑜伽姿势，要求8名参与者对每种瑜伽姿势各进行10次。应用数据增强技术对训练数据集进行过采样并防止过拟合。利用六种迁移学习模型（TL-VGG16-DA、TL-VGG19-DA、TL-MobileNet-DA、TL-MobileNetV2-DA、TL-InceptionV3-DA和TL-DenseNet201-DA）进行分类任务，以便根据评估指标为瑜伽指导系统选择最优模型。结果，TL-MobileNet-DA模型被选为最优模型，其总体准确率为98.43%，灵敏度为98.30%，特异性为99.88%，马修斯相关系数为0.9831。该研究提出了一种瑜伽姿势指导系统，可根据所选的瑜伽姿势指导实时识别用户的瑜伽姿势动作，并能指导他们避免不正确的姿势。
