- E-ISSN2508-7894
- KCI
E-ISSN : 2508-7894
Vol.8 No.1
Abstract
The Recently there has been a growing interest in health care due to the COVID-19 situation. In this paper, we intend to develop a healthcare monitoring system to provide users with smart healthcare systems in line with the healthcare 3.0 era. The system consists of a wireless network between various sensors, Android smartphones, and OLEDs using Bluetooth, and through this, a health care monitoring system capable of collecting user's biometric information and managing health by receiving data values of sensors connected to Arduino. In conclusion, the user's BPM value was calculated using the heart rate sensor, and the exercise intensity can be adjusted through this. In addition, a step derivation algorithm is implemented using an acceleration sensor, and calorie consumption can be measured using the step and weight values. As such, the heart rate, step count, calorie consumption data can be transmitted to a smartphone application through a Bluetooth module and output, and can be output to an OLED for users who are not easy to access the smartphone. This healthcare monitoring system can be applied to various groups and technologies.
Abstract
In this paper, a study was conducted to find a self-diagnosis method to prevent the spread of COVID-19 based on machine learning. COVID-19 is an infectious disease caused by a newly discovered coronavirus. According to WHO(World Health Organization)'s situation report published on May 18th, 2020, COVID-19 has already affected 4,600,000 cases and 310,000 deaths globally and still increasing. The most severe problem of COVID-19 virus is that it spreads primarily through droplets of saliva or discharge from the nose when an infected person coughs or sneezes, which occurs in everyday life. And also, at this time, there are no specific vaccines or treatments for COVID-19. Because of the secure diffusion method and the absence of a vaccine, it is essential to self-diagnose or do a self-diagnosis questionnaire whenever possible. But self-diagnosing has too many questions, and ambiguous standards also take time. Therefore, in this study, using SVM(Support Vector Machine), Decision Tree and correlation analysis found two vital factors to predict the infection of the COVID-19 virus with an accuracy of 80%. Applying the result proposed in this paper, people can self-diagnose quickly to prevent COVID-19 and further prevent the spread of COVID-19.
Abstract
In this paper, it is to identify the effects of differences in interpretation levels depending on the type of brand association and the brain psychological distance on the evaluation of the product of that brand through two experiments. To test our hypotheses empirically, we conducted online survey. We addressed the hypotheses involving the general and relative impact of actual and ideal self-congruence on emotional brand attachment (H1) and explored the effect of product involvement as the moderating variable (H1-1 and H1-2). The goal of this research was to validate the results from involving our basic model and to explore the impact of two additional moderating variables (self-esteem and public self-consciousness: H2). We followed the same procedure. This finding is theoretical to the extent of the interpretation level theory in brand association research by applying the interpretation level theory to the brand association, and provides the meaning that, in practice, it is necessary to utilize the message of different types of brain psychological distance depending on the brand association characteristics that the brand has in defining the brand. In particular, it was confirmed that functional brand associations and symbolic brand annals have representational harmonization, respectively, depending on the low and high levels of interpretation levels.
