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1.
19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021 ; : 879-883, 2021.
Article in English | Scopus | ID: covidwho-1788646

ABSTRACT

While the digital information age has brought us convenience in recent years. It has also brought many security risks. The centralized data storage poses the risk of data leakage. With the breakout of the Novel Coronavirus, it is common for schools to import the health management tools such as the body temperature monitoring system. If these health data are integrated together, it can facilitate school authorities to track students' health status, but centralized management has the risk of widespread data leakage. We propose a decentralized, personal cloud data model. The history of a student's health data will be stored only in each student's personal online datastore managed by Edu Pod, which is a student health information data management application that provides various services and mechanisms to secure the personal data in the Pod. By decentralizing the data to the students' individual Pods, it prevents the occurrence of large-scale data leaks by centralized management. We apply this model to the Campus Health Information System (CHIS) which we are studying in. The student's temperature data collected by face recognition will eventually be stored only in his or her personal Pod, which is a portable educational information storage unit, and the student can share the data with the university and any third-party health management software. The application of a centralized personal cloud data model can help students' personal data not be limited to a centralized server farm and can control their personal data more freely. It can also avoid information leakage and help to protect personal privacy at the same time. © 2021 IEEE.

2.
19th IEEE International Conference on Dependable, Autonomic and Secure Computing, 19th IEEE International Conference on Pervasive Intelligence and Computing, 7th IEEE International Conference on Cloud and Big Data Computing and 2021 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2021 ; : 873-878, 2021.
Article in English | Scopus | ID: covidwho-1788645

ABSTRACT

Currently under the epidemic crisis of COVID-19, campus epidemic prevention has become a hot topic, and temperature detecting equipment has become a necessity in public spaces. However, temperature detection systems that are widely sold on the market are relatively simple and cannot recognize personal identity. Besides, they cannot record individual temperature changes, and they are still inadequate in terms of managing personal health information. In this study, we proposed a system that can meet the needs of campus epidemic prevention called CHIS (Campus Health Information System). In CHIS, an infrared sensor is used for temperature detection and combined with face recognition. The body temperature is recorded while face recognition is performed, and the face ID and the collected real-time body temperature are transmitted to the cloud for viewing and managing by the school. The data will be managed centrally in the cloud and will be cleaned during daily processing. In the end, the student's health data history will be stored only in their personal Pod (Personal online datastore), a decentralized personal cloud data model that prevents the risk of large-scale data leakage due to centralized management. The combination of body temperature detection and face recognition avoids substituting the presence of a real person with photos or pictures, which further enhances security. It also reduces the risk of infection prompted by human detection, which increases safety. © 2021 IEEE.

4.
American Journal of Respiratory and Critical Care Medicine ; 203(9), 2021.
Article in English | EMBASE | ID: covidwho-1277046

ABSTRACT

RATIONALE: Currently, there are over 20,000 COVID-19 positive patients requiring intensive care unit (ICU) care in the United States (US). Even prior to the pandemic, up to 30% of family members of ICU patients experience post-traumatic stress disorder and up to 50% sustain potentially prolonged anxiety and/or depression. Although family bedside engagement improves both short-and long-term outcomes for patients and their families, nationwide social distancing recommendations have curtailed hospital visitation, potentially heightening the risk of stress-related disorders in these family members. The goal of this analysis is to explore the experiences of physically distanced family members of COVID-19 ICU patients in order to inform future best practices. Methods: This qualitative analysis is part of a multisite, observational, mixed-methods study of 12 US hospitals. Qualitative interviews were conducted with 75 participants from five sites;14 interviews were analyzed in this preliminary analysis. Adult family members of COVID-19 positive patients admitted to the ICU from March-June 2020 were interviewed three months post-discharge. After sequential screening by site coordinators, participants were contacted by the qualitative team until all interviews (10-15 per site) were completed. Qualitative interviews explored the illness stories, communication perceptions, and explored stressors. Thematic analysis was applied to the verbatim transcripts of the phone interviews. Four coders utilized an iteratively-developed codebook to analyze transcripts using a round-robin method with two analysts per transcript. Discrepant codes were adjudicated by a third analyst to attend to inter-rater reliability. Results: Five preliminary themes and seven subthemes emerged (Table 1). Positive communication experiences were more common than negative ones. Communication themes were: 1) Participants were reassured by proactive and frequent communication, leaving them feeling informed and included in care;and 2) Mixed feelings were expressed about the value of video-conferencing technology. Themes from the emotional and stress experiences were: 3) Profound sadness and distress resulted from isolation from patients, clinicians, and supportive family;4) Stress was amplified by external factors;and 5) Positive experiences centered upon appreciation for healthcare workers and gratitude for compassionate care. Conclusion: Incorporating the voices of family members during the COVID-19 pandemic establishes a foundation to inform family-centered, best practice guidelines to support the unique needs of family members who are physically distant from their critically ill and dying loved ones.

5.
IEEE Access ; 2021.
Article in English | Scopus | ID: covidwho-1225647

ABSTRACT

Eye state evaluation is crucial for vision-based driver fatigue detection. With the outbreak of COVID-19, many proposed models for eye location and state evaluation based on facial landmarks are unreliable due to mask coverings. In this paper, we proposed a robust facial landmark location model for eye location and state evaluation. First, we develop an existing lightweight face alignment model for eye key point locations that is robust in large poses. Then, to develop the performance of our model in a complex driving environment such as an environment with mask coverings, changing illumination, etc., we design a method to augment the training data set based on the original landmark data set without any extra cost. Finally, some facial landmarks around the eyes are extracted, and the eye aspect ratio (EAR) is introduced to evaluate the eye state based on eye key points. The experiment shows that our model achieves significantly improved landmark location performance on a driving simulation data set due to data augmentation. We tested our model on the BioID data set to measure the eye state evaluation performance, and the results showed that our model obtained satisfactory performance with an accuracy of approximately 97.7%. Further testing on the driving simulation data set shows that our model is robust in different driving scenarios with an average accuracy of approximately 93.9%. CCBY

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