ABSTRACT
Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7% and stop auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants. © 2023 IEEE.
ABSTRACT
This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages coordinated multi-views to support exploratory and predictive analysis of hospitalizations and other socio-geographic variables at multiple dimensions, simultaneously. By harnessing the strength of geometric deep learning, we build a consensus machine learning model to include knowledge from county-level records and investigate the complex interrelationships between global infectious disease, environment, and social justice. Additionally, we make use of unique NASA satellite-based observations which are not broadly used in the context of climate justice applications. Our current interactive interface focus on three US states (California, Pennsylvania, and Texas) to demonstrate its scientific value and presented three case studies to make qualitative evaluations. © 2022 IEEE.
ABSTRACT
In this paper, Covid Explorer model's aim is to provide information on Covid 19 like-how you can prevent the Corona Virus, symptoms of Corona Virus or how to book slots for the Corona Virus. You can add the current data related to Covid 19 like a graph indicated the increase or decrease of death rate from this virus, a report is given to show the death rate, the number of vaccines doses given on a particular day, state wise cases are also shown through Application Programming Interface (API) in Covid Explorer model. The model is analyzing and tracking Corona Virus. As per the data analysis this pandemic creates mental health issues but if a model gave up-to date data of the current scenario then stress can be overcome and society can fight against this pandemic. © 2022 IEEE.
ABSTRACT
We design a central controller system (CCS) and a tele-controlled system (TCS) with an aim of developing the integrated tele-monitoring/operation system that can enable the medical staff to tele-monitor the state of therapeutic devices utilized in the isolation intensive care unit (ICU) and to tele-operate its user interfaces. To achieve this aim, we survey the medical staff for medical requirements first and define the design guideline for tele-monitoring/operation functionality and field applicability. In designing the CCS, we focus on realizing the device having intuitive and user-friendly interfaces so that the medical staff can use the device conveniently without pre-training. Further, we attempt to implement the TCS capable of manipulating various types of user interfaces of the therapeutic device (e.g., touch screen, buttons, and knobs) without failure. As two core components of the TCS, the precision XY-positioner having a maximum positioning error of about 0.695 mm and the end-effector having three-degrees-of-freedom motion (i.e., pressing, gripping, and rotating) are applied to the system. In the experiment conducted for assessing functionality, it is investigated that the time taken to complete the tele-operation after logging into the CCS is less than 1 minute. Furthermore, the result of field demonstration for focus group shows that the proposed system could be applied practically to the medical fields when the functional reliability is improved. IEEE