ABSTRACT
Every person spends around one third of his/her life in bed. For an infant or a young toddler, this percentage can be much higher, and for bed-bound patients it can go up to 100% of their time. In-bed pose estimation is a critical step in many human behavior monitoring systems that are focused on prevention, prediction, and management of at-rest or sleep-related conditions in both adults and children. The topic of automatic noncontact human pose estimation has received a lot of attention/success, especially in the last few years in the computer vision community, thanks to the introduction of deep learning and its power in artificial intelligence (AI) modeling. However, the state-of-the-art (SOTA) vision-based AI algorithms in this field can hardly work under the challenges associated with in-bed human behavior monitoring, such as significant illumination changes (e.g., full darkness at night), heavy occlusion (e.g., covering by a sheet or blanket), as well as the privacy concerns that mitigate large-scale data collection, necessary for any deep learning-based model training. The data quality challenges and privacy concerns have hindered the use of advanced vision-based in-bed behavior monitoring systems at home, which during the recent COVID-19 pandemic could have been an effective way to control the spread of the virus by avoiding in-person visits to clinics.
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic is wreaking havoc on public-health and economic systems worldwide. Among the several neurological symptoms of patients with COVID-19 reported in clinical practice, olfactory dysfunction (OD) is the most common. OD occurs as the earliest or the only clinical manifestation in some patients. Increasing research attention has focused on OD, which is listed as one of the main diagnostic symptoms of severe acute respiratory syndrome-coronavirus-2 infection. Multiple clinical and basic-science studies on COVID-19-induced OD are underway to clarify the underlying mechanism of action. In this review, we summarize the clinical characteristics, mechanisms, evaluation methods, prognosis, and treatment options of COVID-19-induced OD. In this way, we hope to improve the understanding of COVID-19-induced OD to aid early identification and precise intervention.
ABSTRACT
OBJECTIVE: Wuhan was the first Chinese city to be lockdown for the coronavirus disease 2019 (COVID-19) outbreak in springtime of 2020. The purpose of this study was to investigate the relationship between sleep status, body mass index, anxiety and depression in college students during the post-pandemic era in the universities of Wuhan, China. METHODS: A total of 1457 college students completed the online surveys from December 25, 2020 to January 16, 2021. Collected data included gender, age, school name, native place, grade, major, body mass index (BMI), the scores of self-assessment lists of sleep (SRSS), Zung self-rating anxiety scale (SAS) and Zung depression self-rating scale (SDS). RESULTS: 1445 valid questionnaires (99.18%) were received. Of all the respondents, the prevalence of insomnia, overweight and obesity, anxiety and depression were 32.73%, 19.45%, 15.43% and 62.91%, respectively. Female students were more likely to have insomnia and anxiety than male students. The rate of insomnia, overweight and obesity in postgraduates were higher than undergraduates. Non-medical students were more likely to be overweight and obese than medical students. In addition, insomnia severity was positively correlated to anxiety severity, and BMI was positively correlated to anxiety or depression severity. There was also a positive correlation between the severity of anxiety and depression. CONCLUSION: During the post-pandemic era, insomnia and depression are common problems among college students in Wuhan, suggesting that we should strengthen the sleep education of college students to improve sleeping disorders and psychosomatic health.