Human Posture Detection Using Image Augmentation and Hyperparameter-Optimized Transfer Learning Algorithms

With the advancement in pose estimation techniques, human posture detection recently received considerable attention many applications, including ergonomics and healthcare. When using neural network models, overfitting poor performance are prevalent issues. Recently, convolutional networks (CNNs) were successfully used for recognition from images due to their superior multiscale high-level visual representations over hand-engineering low-level characteristics. However, calculating millions of parameters a deep CNN requires significant number annotated examples, which prohibits CNNs such as AlexNet VGG16 being on issues with minimal training data. We propose new three-phase model decision support that integrates transfer learning, image data augmentation, hyperparameter optimization (HPO) address this problem. The is part framework hyperparameters AlexNet, VGG16, CNN, multilayer perceptron (MLP) models accomplishing optimal classification results. learning algorithms HPO detection, while Multilayer Perceptron standard classifiers contrast. methods essential machine because they directly influence behaviors have major impact models. an augmentation technique increase be reduce improve MLP combination was found four random-based search strategy. MPII datasets test proposed approach. achieved accuracy 91.2% 90.2% 87.5% 89.9% MLP. study first executed dataset.