Transfer Learning Based Yogic Posture Recognition System Using Deep Pre-trained Features

Yoga is a healthy exercise that focuses on physical, psychological, and divine connections. However, engaging in yoga while adopting poor postures might result in health issues like muscle discomfort and sprains. Discovering the specific posture amid an activity is still a very challenging and complicated task in artificial intelligence. With the aid of transfer learning, a model which can identify the yogic posture from images has been constructed in this work. In this approach, sixteen postures of yogic data are collected, and then pre-processing techniques are used to resize the images. This work employs the transfer learning method to extract the features from popular convolutional neural network architectures such as VGG16, VGG19, InceptionV3, Xception, MobileNet, and MobileNetV2; all are pre-trained on the ImageNet database. Extracted features are fed to machine learning models like Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) for classification. The linear SVM-based classifier, trained on features extracted by MobileNet, performs better than its rivals with an accuracy of 99.92%. In total, six transfer learning models are exploited in this yogic posture classification approach and yield encouraging results in terms of prediction accuracy.