###### Abstract  
With the advancement in pose estimation techniques, human posture detection recently received considerable attention in many applications, including ergonomics and healthcare. When using neural network models, overfitting and poor performance are prevalent issues. Recently, convolutional neural networks (CNNs) were successfully used for human posture recognition from human images due to their superior multiscale high-level visual representations over hand-engineering low-level characteristics. However, calculating millions of parameters in a deep CNN requires a significant number of annotated examples, which prohibits many deep CNNs such as AlexNet and VGG16 from being used on issues with minimal training data. We propose a new three-phase model for decision support that integrates CNN transfer learning, image data augmentation, and hyperparameter optimization (HPO) to address this problem. The model is used as part of a new decision support framework for the optimization of hyperparameters for AlexNet, VGG16, CNN, and multilayer perceptron (MLP) models for accomplishing optimal classification results. The AlexNet and VGG16 transfer learning algorithms with HPO are used for human posture detection, while CNN and Multilayer Perceptron (MLP) were used as standard classifiers for contrast. The HPO methods are essential for machine learning and deep learning algorithms because they directly influence the behaviors of training algorithms and have a major impact on the performance of machine learning and deep learning models. We used an image data augmentation technique to increase the number of images to be used for model training to reduce model overfitting and improve classification performance using the AlexNet, VGG16, CNN, and MLP models. The optimal combination of hyperparameters was found for the four models using a random-based search strategy. The MPII human posture datasets were used to test the proposed approach. The proposed models achieved an accuracy of 91.2% using AlexNet, 90.2% using VGG16, 87.5% using CNN, and 89.9% using MLP. The study is the first HPO study executed on the MPII human pose dataset. data augmentation; CNN; dropout; transfer learning; human pose detection; hyperparameter optimization; decision support +
Footnote †: journal: Applied  
over-fitting preventive strategy is used, learning that many parameters from just thousands of training samples will result in substantial over-fitting. As a result, it is difficult to fit a deep CNN to a small dataset while maintaining equivalent performance to a large dataset. Transfer learning, which drops the classifier layer of a pre-trained CNN and fine-tunes it on the target dataset, is a typical approach for employing deep CNNs on short datasets. This is an efficient strategy in practice. While this strategy can be used to solve a specific problem, it leaves us with some unanswered questions: how to choose the fine-tuning strategy, how to set the hyperparameters (e.g., learning rate) for network fine-tuning, and additional approaches to increase the network performance [7]. Several manuscripts have used machine learning and deep learning methods to solve the human posture classification problem [9; 10], but none applied hyperparameter optimization (HPO) with algorithms to find the best hyperparameters that will induce the most excellent classification accuracy for the machine learning or deep learning algorithms used (as far as we know). This set of hyperparameters is not the same for every classification task and changes according to the nature of the medical problem, which is complex by design. When a critical data analysis of medical data is required to identify hidden links or abnormalities that are not evident to humans, machine learning technologies are being employed in healthcare for computational decision-making [11].