skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: AI Based Remotely monitoring Web User Interface to capture patient temperature and medicine consumption
The Coronavirus disease 2019 (COVID-19) pandemic has impacted the world like no other pandemic in history. COVID-19 has progressed rapidly affecting health care in the global community. In this work, we look at the web user interface for elderly patients to easily keep track of their daily temperature and their pill consumption record and see if there is a need to visit a doctor. We are going to use Efficient Net, a Deep Learning Convolution Neural Network (CNN) architecture for systematical use of model scaling and balancing network depth, width, and resolution for better performance of our model. We are using angular components to develop user interface and expressJS to write backend services and MongoDB to store user records for the application which we learn in depth throughout the paper. we have trained and developed a model that could successfully detect people from the video and confirm the pill consumption by patient with high accuracy rate connected to a THT thermal camera. This study will help medical professionals, especially during the COVID-19 pandemic, in remotely monitoring the patient’s dosage and determining appropriate action in case of non-compliance.  more » « less
Award ID(s):
2028612
PAR ID:
10443606
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
International Supply Chain Technology Journal
Volume:
7
Issue:
12
ISSN:
2380-5730
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; (, International Supply Chain Technology Journal)
    The Coronavirus disease 2019 (COVID-19) pandemic has impacted the world like no other pandemic in history. COVID-19 has progressed rapidly affecting health care in the global community. In this paper, we study the Image classification approach for confirming the consumption of medicine by people. The motivation for this research is to reduce contact with the people, without having them in physical observation and help minimize the spread of COVID-19. We used EfficientNet, a Deep Learning Convolution Neural Network (CNN) [7] architecture for systematical use of model scaling and balancing network depth, width, and resolution for better performance of our model. In this paper, we used the data augmentation technique to avoid overfitting. We used image hashing to compare and remove relatively similar images as data cleaning step and demonstrate the effectiveness of this approach by observing the classification accuracy. We used cloud computation to train the model. This trained model will be helpful to achieve the goal. In this study, we developed a model that could successfully detect the image and confirm the pill consumption by patient with high accuracy rate. This study will help medical professionals, especially during COVID-19 pandemic, in remotely monitoring the patient’s dosage and determine appropriate action in case of non-compliance. 
    more » « less
  2. ; ; (, Psychology, Health & Medicine)
    Beginning in early 2020, the novel coronavirus was the subject of frequent and sustained news coverage. Building on prior literature on the stress-inducing effects of consuming news during a large-scale crisis, we used network analysis to investigate the association between coronavirus disease 2019 (COVID-19) news consumption, COVID-19-related psychological stress, worries about oneself and one’s loved ones getting COVID-19, and sleep quality. Data were collected in March 2020 from 586 adults (45.2% female; 72.9% White) recruited via Amazon Mechanical Turk in the U.S. Participants completed online surveys assessing attitudes and behaviors related to COVID-19 and a questionnaire assessing seven domains of sleep quality. Networks were constructed using partial regularized correlation matrices. As hypothesized, COVID-19 news consumption was positively associated with COVID-19-related psychological stress and concerns about one’s loved ones getting COVID-19. However, there were very few associations between COVID-19 news consumption and sleep quality indices, and gender did not moderate any of the observed relationships. This study replicates and extends previous findings that COVID-19-news consumption is linked with psychological stress related to the pandemic, but even under such conditions, sleep quality can be spared due to the pandemic allowing for flexibility in morning work/school schedules. 
    more » « less
  3. ; ; (, Socius: Sociological Research for a Dynamic World)
    The authors explore media distrust among a sample of precarious and gig workers interviewed during the COVID-19 pandemic. Although these left-leaning respondents initially increased their media consumption at the outset of the pandemic, they soon complained of media sensationalism and repurposed a readily available cultural tool: claims of “fake news.” As a result, these unsettled times have resulted in a “diffusion of distrust,” in which an elite conservative discourse of skepticism toward the media has also become a popular form of compensatory control among self-identified liberals. Perceiving “fake news” and media sensationalism as “not good” for their mental health, respondents also reported experiencing media burnout and withdrawing from media consumption. As the pandemic passes its one-year anniversary, this research has implications for long-term media coverage on COVID-19 and ongoing media trust and consumption. 
    more » « less
  4. (, the Proceedings of the 21st International Conference on Human-Computer Interaction (HCII 2021))
    null (Ed.)
    Since the start of coronavirus disease 2019 (COVID-19) pandemic, social media platforms have been filled with discussions about the global health crisis. Meanwhile, the World Health Organization (WHO) has highlighted the importance of seeking credible sources of information on social media regarding COVID-19. In this study, we conducted an in-depth analysis of Twitter posts about COVID-19 during the early days of the COVID-19 pandemic to identify influential sources of COVID-19 information and understand the characteristics of these sources. We identified influential accounts based on an information diffusion network representing the interactions of Twitter users who discussed COVID-19 in the United States over a 24-h period. The network analysis revealed 11 influential accounts that we categorized as: 1) political authorities (elected government officials), 2) news organizations, and 3) personal accounts. Our findings showed that while verified accounts with a large following tended to be the most influential users, smaller personal accounts also emerged as influencers. Our analysis revealed that other users often interacted with influential accounts in response to news about COVID-19 cases and strongly contested political arguments received the most interactions overall. These findings suggest that political polarization was a major factor in COVID-19 information diffusion. We discussed the implications of political polarization on social media for COVID-19 communication. 
    more » « less
  5. ; (, Journal of Systemics, Cybernetics and Informatics)
    The COVID-19 pandemic has significantly impacted most countries in the world. Analyzing COVID-19 data from these countries together is a prominent challenge. Under the sponsorship of NSF REU, this paper describes our experience with a ten-week project that aims to guide an REU scholar to develop a physics-guided graph attention network to predict the global COVID- 19 Pandemics. We mainly presented the preparation, implementation, and dissemination of the addressed project. The COVID-19 situation in a country could be dramatically different from that of others, which suggests that COVID-19 pandemic data are generated based on different mechanisms, making COVID-19 data in different countries follow different probability distributions. Learning more than one hundred underlying probability distributions for countries in the world from large scale COVID- 19 data is beyond a single machine learning model. To address this challenge, we proposed two team-learning frameworks for predicting the COVID-19 pandemic trends: peer learning and layered ensemble learning framework. This addressed framework assigns an adaptive physics-guided graph attention network (GAT) to each learning agent. All the learning agents are fabricated in a hierarchical architecture, which enables agents to collaborate with each other in peer-to-peer and cross-layer way. This layered architecture shares the burden of large-scale data processing on machine learning models of all units. Experiments are run to verify the effectiveness of our approaches. The results indicate the proposed ensemble outperforms baseline methods. Besides being documented on GitHub, this work has resulted in two journal papers. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email