In this article, we introduce a new approach to human movement by defining the movement as a static super object represented by a single two-dimensional image. The described method is applicable in remote healthcare applications, such as physiotherapeutic exercises. It allows researchers to label and describe the entire exercise as a standalone object, isolated from the reference video. This approach allows us to perform various tasks, including detecting similar movements in a video, measuring and comparing movements, generating new similar movements, and defining choreography by controlling specific parameters in the human body skeleton. As a result of the presented approach, we can eliminate the need to label images manually, disregard the problem of finding the start and the end of an exercise, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one illustrates how to verify and score a fitness exercise. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supplying sufficient training data for deep learning applications (DL). A variational auto encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate the two use cases. These use cases demonstrate the versatility of our innovative concept in measuring, categorizing, inferring human behavior, and generating gestures for other researchers.

• Klíčová slova
• MediaPipe (MP), OpenPose (OP), Siamese twin neural network, computational creativity, computational imagination, human body movements, human pose estimation (HPE), rehabilitation, simulator, tree structure skeleton color image (TSSCI), tree structure skeleton image (TSSI), variational auto encoder (VAE),
• Publikační typ
• časopisecké články MeSH

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

• Klíčová slova
• ANFIS, Adaptive Network-based Fuzzy Inference System, ANN, Artificial Neural Network, AU, Australia, Bi-Conv-LSTM, Bidirectional Convolutional Long Short Term Memory, Bi-GRU, Bidirectional Gated Recurrent Unit, Bi-LSTM, Bidirectional Long Short-Term Memory, Bidirectional, COVID-19 Prediction, COVID-19, Coronavirus Disease 2019, Conv-LSTM, Convolutional Long Short Term Memory, Convolutional Long Short Term Memory (Conv-LSTM), DL, Deep Learning, DLSTM, Delayed Long Short-Term Memory, Deep learning, EMRO, Eastern Mediterranean Regional Office, ES, Exponential Smoothing, EV, Explained Variance, GRU, Gated Recurrent Unit, Gated Recurrent Unit (GRU), IR, Iran, LR, Linear Regression, LSTM, Long Short-Term Memory, Lasso, Least Absolute Shrinkage and Selection Operator, Long Short Term Memory (LSTM), MAE, Mean Absolute Error, MAPE, Mean Absolute Percentage Error, MERS, Middle East Respiratory Syndrome, ML, Machine Learning, MLP-ICA, Multi-layered Perceptron-Imperialist Competitive Calculation, MSE, Mean Square Error, MSLE, Mean Squared Log Error, Machine learning, New Cases of COVID-19, New Deaths of COVID-19, PRISMA, Preferred Reporting Items for Precise Surveys and Meta-Analyses, RMSE, Root Mean Square Error, RMSLE, Root Mean Squared Log Error, RNN, Repetitive Neural Network, ReLU, Rectified Linear Unit, SARS, Serious Intense Respiratory Disorder, SARS-COV, SARS coronavirus, SARS-COV-2, Serious Intense Respiratory Disorder Coronavirus 2, SVM, Support Vector Machine, VAE, Variational Auto Encoder, WHO, World Health Organization, WPRO, Western Pacific Regional Office,
• Publikační typ
• časopisecké články MeSH