ABSTRACT
Blood oxygen saturation (SpO(2)) is an important indicator forpulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO(2). Most of these works are contact-based, requiring users to cover a phone's camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO(2) monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO(2) information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO(2) prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.
ABSTRACT
Group testing can save testing resources in the context of the ongoing COVID-19 pandemic. In group testing, we are given n samples, one per individual, and arrange them into m < n pooled samples, where each pool is obtained by mixing a subset of the n individual samples. Infected individuals are then identified using a group testing algorithm. In this paper, we use side information (SI) collected from contact tracing (CT) within nonadaptive/single-stage group testing algorithms. We generate data by incorporating CT SI and characteristics of disease spread between individuals. These data are fed into two signal and measurement models for group testing, where numerical results show that our algorithms provide improved sensitivity and specificity. While Nikolopoulos et al. utilized family structure to improve nonadaptive group testing, ours is the first work to explore and demonstrate how CT SI can further improve group testing performance.
ABSTRACT
Coronavirus disease 2019 (COVID-19) disproportionately affects older people in Hong Kong. In Hong Kong, as of 17 July 2020, of the 10 recorded mortalities, seven involved patients aged >70 years. Social distancing as a strategy to limit the spread of COVID-19 has restricted the access to health and community services and has induced social isolation for older people. Hospital practice became less elderly friendly when infection control took priority over humanistic considerations. In 2004, The Hong Kong Geriatrics Society published a position statement on management of older patients with severe acute respiratory syndrome. Those guidelines were also followed during the current COVID-19 pandemic. Herein, we report our experience of caring for older people in Hong Kong during the COVID-19 pandemic, including the effect on older people of restricted access to health and community services, infection control measures in residential care homes for the elderly, treatment of older patients after admission to hospitals, end-of-life care, and the emergence of telecare.