Objective This study aimed to introduce a 4-week long fully immersive virtual reality-based cognitive training (VRCT) program that could be applied for both a cognitively normal elderly population and patients with mild cognitive impairment (MCI). In addition, we attempted to investigate the neuropsychological effects of the VRCT program in each group.Methods A total of 56 participants, 31 in the MCI group and 25 in the cognitively normal elderly group, underwent eight sessions of VRCT for 4 weeks. In order to evaluate the effects of the VRCT, the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet was administered before and after the program. The program’ s safety was assessed using a simulator sickness questionnaire (SSQ), and availability was assessed using the presence questionnaire.Results After the eighth session of the VRCT program, cognitive improvement was observed in the ability to learn new information, visuospatial constructional ability, and frontal lobe function in both groups. At the baseline evaluation, based on the SSQ, the MCI group complained of disorientation and nausea significantly more than the cognitively normal elderly group did. However, both groups showed a reduction in discomfort as the VRCT program progressed.Conclusion We conclude that our VRCT program helps improve cognition in both the MCI group and cognitively normal elderly group. Therefore, the VRCT is expected to help improve cognitive function in elderly populations with and without MCI.

We examine an application of machine learning to exchange traded fund investments in the U.S. market. To find how the changes in exchange traded fund prices are associated with expected market fundamentals, we propose three parsimonious risk factors extracted from various U.S. economic and market indicators. Based on the information set including these three factors, we build a predictive support vector machine model that can detect long or short investment signals. We find that the high probability of an upward momentum from our forecasting model suggests a long exchange traded fund signal, whereas the low probability of a downward momentum indicates a short exchange traded fund signal. We further design an algorithmic trading system with the support vector machine factor model. We find that the trading system shows practically desirable and robust performances over in-sample and out-of-sample trading periods

This study applies machine learning methods to develop a sustainable pairs trading market-neutral investment strategy across multiple futures markets. Cointegrated pairs with similar price trends are identified, and a hedge ratio is determined using an Error Correction Model (ECM) framework and support vector machine algorithm based upon the two-step Engle–Granger method. The study shows that normal backwardation and contango do not consistently characterize futures markets, and an algorithmic pairs trading strategy is effective, given the unique predominant price trends of each futures market. Across multiple futures markets, the pairs trading strategy results in larger risk-adjusted returns and lower exposure to market risk, relative to an appropriate benchmark. Backtesting is employed and results show that the pairs trading strategy may hedge against unexpected negative systemic events, specifically the COVID-19 pandemic, remaining profitable over the period examined.

This study aimed to verify the effectiveness of virtual reality (VR) cognitive training by measuring the sense of presence and electroencephalography (EEG) in children with ADHD. A clinical trial was conducted to verify the effect of VR cognitive training on children with ADHD. The experimental group included eight children with ADHD, and the control group included eight healthy children without ADHD. The sense of presence increased significantly after the VR cognitive training in children with ADHD. Also, no significant changes in the alpha, beta, delta, and gamma wave amplitudes were found in both groups after the VR cognitive training. Thus, the VR training content developed in this study can help measure the patients’ behavioral inhibition, increase their sense of presence by inducing interaction with distraction stimuli, and alleviate their symptoms. However, EEG could only be used as an auxiliary means, at the clinician’s judgment, for ADHD diagnosis in children because no significant EEG changes were observed in the experimental or control groups after the VR cognitive training.

Metaverse technology is expanding to industries in various fields, such as medical, national defense, and education, and training simulation programs have been mainstream so far. However, there have been increasing attempts to apply metaverse content to web-based platforms linked to social media services and, as a result, we face the problem of access to web-based metaverse content. Unlike traditional content, metaverse content interacts with many users, so content accessibility is the first important part to consider. In other words, to maximize the quality of metaverse content, it is essential to pull out the optimal UX through a detailed HCI (human computer interaction) design. Metaverse content development methodologies have effective methods proposed by many researchers. However, they are limited to web-based metaverse content that limits the use of high-end hardware. They are ineffective for platforms such as PCs and VR devices, as most studies focus on improving the visual performance of PCs or high-performance VR devices. Therefore, unlike existing research, the key theme of our research is to study optimized development standards that can be applied to web-based metaverse content and find out their effects through experiments. We created a development standard to be applied to a Web-based platform based on the existing metaverse content development methodology. Then, we redeveloped the VR content into the metaverse content and named them the VR build and the metaverse build. We had 25 people play virtual reality builds and metaverse builds simultaneously. Then, we measured the overall experience with an evaluation tool called the Game Experience Questionnaire (GEQ); the GEQ is a proven tool for evaluating content experiences by dividing them into positive/negative scales. When comparing the results measured from the two builds, the metaverse build showed consistent results with a higher positive scale, and a lower negative scale, than the VR build. The results showed that users indeed rated metaverse content positively. The bottom line is that the web-based metaverse content development standards that we have produced are practical. However, since generalization is limited, continuous research will be needed in more experimental groups in the future.

Along with the development of IT technology, the healthcare industry is growing by converging with IT technology, and studies on various fusion medical clinical trials using virtual reality technology, which have been particularly noticeable after the 4th industrial revolution, are actively progressing. In particular, there is a trend of active research on strabismus treatment in combination with virtual reality technology at home and abroad. In this paper, through collaboration with an ophthalmologist, we developed 3 types of mini-game contents that can be used to train strabismus patients in virtual reality space by wearing an HMD, and we study how to control prism diopter value to obtain strabismus training effect in general virtual reality games.