Development of a yoga posture coaching system using an interactive display based on transfer learning.
Development of a yoga posture coaching system using an interactive display based on transfer learning.
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DOI：
10.1007/s11227-021-04076-w
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Nam Y
中科院分区：
文献类型：
--
作者：
Long C;Jo E;Nam Y
瑜伽是一种有益于健康的运动形式，专注于身体，心理和精神联系。然而，练习瑜伽和采用不正确的姿势会导致健康问题，如肌肉扭伤和疼痛。在这项研究中，我们提出了一个瑜伽姿势教练系统的发展，使用交互式显示，基于迁移学习技术。14种不同的瑜伽姿势是从RGB相机中收集的，8名参与者被要求执行每个瑜伽姿势10次。应用数据增强以过采样并防止训练数据集的过拟合。六个迁移学习模型（TL-VGG16-DA，TL-VGG19-DA，TL-MobileNet-DA，TL-MobileNetV2-DA，TL-InceptionV3-DA和TL-DenseNet201-DA）被用于分类任务，以基于评估指标为瑜伽教练系统选择最佳模型。结果，选择TL-MobileNet-DA模型作为最佳模型，其总体准确性为98.43%，敏感性为98.30%，特异性为99.88%，马修斯相关系数为0.9831。提出了一种瑜伽姿势指导系统，能够真实的识别用户的瑜伽姿势动作，根据所选择的瑜伽姿势进行指导，并能指导用户避免错误的姿势。
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.
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DOI：
10.1080/17457300.2015.1032981
发表时间：
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影响因子：
2.3
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