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This paper proposes a hand interface through a novel deep learning that provides easy and realistic interactions with hands in immersive virtual reality. The proposed interface is designed to provide a real-to-virtual direct hand interface using a controller to map a real hand gesture to a virtual hand in an easy and simple structure. In addition, a gesture-to-action interface that expresses the process of gesture to action in real-time without the necessity of a graphical user interface (GUI) used in existing interactive applications is proposed. This interface uses the method of applying image classification training process of capturing a 3D virtual hand gesture model as a 2D image using a deep learning model, convolutional neural network (CNN). The key objective of this process is to provide users with intuitive and realistic interactions that feature convenient operation in immersive virtual reality. To achieve this, an application that can compare and analyze the proposed interface and the existing GUI was developed. Next, a survey experiment was conducted to statistically analyze and evaluate the positive effects on the sense of presence through user satisfaction with the interface experience.

Sudden cardiac arrest can leave serious brain damage or lead to death, so it is very important to predict before a cardiac arrest occurs. However, early warning score systems including the National Early Warning Score, are associated with low sensitivity and false positives. We applied shallow and deep learning to predict cardiac arrest to overcome these limitations. We evaluated the performance of the Synthetic Minority Oversampling Technique Ratio. We evaluated the performance using a Decision Tree, a Random Forest, Logistic Regression, Long Short-Term Memory model, Gated Recurrent Unit model, and LSTM–GRU hybrid models. Our proposed Logistic Regression demonstrated a higher positive predictive value and sensitivity than traditional early warning systems.

The early warning system detects early and responds quickly to emergencies in high-risk patients, such as cardiac arrest in hospitalized patients. However, traditional early warning systems have the problem of frequent false alarms due to low positive predictive value and sensitivity. We conducted early prediction research on cardiac arrest using time-series data such as biosignal and laboratory data. To derive the data attributes that affect the occurrence of cardiac arrest, we performed a correlation analysis between the occurrence of cardiac arrest and the biosignal data and laboratory data. To improve the positive predictive value and sensitivity of early cardiac arrest prediction, we evaluated the performance according to the length of the time series of measured biosignal data, laboratory data, and patient data range. We propose a machine learning and deep learning algorithm: the decision tree, random forest, logistic regression, long short-term memory (LSTM), gated recurrent unit (GRU) model, and the LSTM–GRU hybrid model. We evaluated cardiac arrest prediction models. In the case of our proposed LSTM model, the positive predictive value was 85.92% and the sensitivity was 89.70%.

Particulate matter (PM) has become a problem worldwide, with many deleterious health effects such as worsened asthma, affected lungs, and various toxin-induced cancers. The International Agency for Research on Cancer (IARC) under the World Health Organization (WHO) has designated PM as a group 1 carcinogen. Although Korea Environment Corporation forecasts the status of outdoor PM four times a day, whichever is higher among PM10 and PM2.5. Korea Environment Corporation forecasts for the stages of PM. It remains difficult to predict the value of PM when going out. We correlate air quality and solar terms, address format, and weather data, and PM in the Republic of Korea. We analyzed the correlation between address format, air quality data, and weather data, and PM. We evaluated performance according to the sequence length and batch size and found the best outcome with a sequence length of 7 days, and a batch size of 96. We performed PM prediction using the Long Short-Term Recurrent Unit (LSTM), the Convolutional Neural Network (CNN), and the Gated Recurrent Unit (GRU) models. The CNN model suffered the limitation of only predicting from the training data, not from the test data. The LSTM and GRU models generated similar prediction results. We confirmed that the LSTM model has higher accuracy than the other two models.