Evaluation of the Microbiota and Integrity of Respirators Reused by Health Professionals in a Hospital Environment

The shortage of PFF2, N95, and KN95 respirators and their equivalents for the respiratory protection of the population and health professionals during COVID-19 pandemic has driven the adoption of alternative measures to address the lack of personal protective equipment (PPE). The use of surgical masks, handmade masks, and even the prolonged use of respirators were some of the measures adopted in response to the high demand for these products, and their consequent shortage. In this context, the present study evaluated the microbiota and integrity of reused PFF2 respirators in the central sterile services department of a hospital. Respirators that had been used for 0 h, 12 h, 24 h, and 36 h were sampled for the inoculation and cultivation of fungi and bacteria and the identification of their microbiota. To assess the integrity of the respirators, a filtration efficiency assessment test was conducted of the respirators used for 36 h. The results obtained showed that the microbiota of the respirators comprised commensal fungi and bacteria from the oral and nasal regions of human beings. It was also found that after 36 h of use, the respirators did not demonstrate a decrease in filtration efficiency;that is, they retained their 97% filtration efficiency. Considering the findings regarding the presence and pathogenicity of microorganisms, it is possible that the reuse of respirators for up to 36 h does not harm the health of immunocompetent users. In terms of PPE efficiency, no compromises were evidenced.

Internet-Based Video Program to Promote Physical Activity, Health, and Well-Being of Brazilian Older Adults during the COVID-19 Pandemic

In 2020 and 2021, the SARS-CoV-2 coronavirus spread rapidly across the world, causing the COVID-19 pandemic with millions of deaths. One of the measures to protect life was confinement, which negatively affected physical and mental health, especially of the older population. The aim of this study is to present and evaluate the methodological procedures of a telehealth and eHealth program "U3A in Motion”, which was composed of videos of physical exercises and activities to promote the mental health and well-being of the older Brazilian population during the COVID-19 pandemic. The procedures included the planning, editing, and dissemination of videos through WhatsApp, and also on the YouTube platform, Instagram, and on a website. A total of 82 videos were created. The action reached 350 older adults from the local community in the northeast of Brazil, as well as being accessed by approximately 3000 other older adults from institutions in the southern region of Brazil. Based on the evaluation of activities through telephone interviews, it was found that older adults participating in the "U3A in Motion” program during confinement were highly motivated to access exercise activities, mainly via mobile phones, and reported a positive effect on physical and mental health. © 2023 by the authors.

Beyond Masks: On the Generalization of Masked Face Recognition Models to Occluded Face Recognition

Over the years, the evolution of face recognition (FR) algorithms has been steep and accelerated by a myriad of factors. Motivated by the unexpected elements found in real-world scenarios, researchers have investigated and developed a number of methods for occluded face recognition (OFR). However, due to the SarS-Cov2 pandemic, masked face recognition (MFR) research branched from OFR and became a hot and urgent research challenge. Due to time and data constraints, these models followed different and novel approaches to handle lower face occlusions, i.e., face masks. Hence, this study aims to evaluate the different approaches followed for both MFR and OFR, find linked details about the two conceptually similar research directions and understand future directions for both topics. For this analysis, several occluded and face recognition algorithms from the literature are studied. First, they are evaluated in the task that they were trained on, but also on the other. These methods were picked accordingly to the novelty of their approach, proven state-of-the-art results, and publicly available source code. We present quantitative results on 4 occluded and 5 masked FR datasets, and a qualitative analysis of several MFR and OFR models on the Occ-LFW dataset. The analysis presented, sustain the interoperable deployability of MFR methods on OFR datasets, when the occlusions are of a reasonable size. Thus, solutions proposed for MFR can be effectively deployed for general OFR. © 2022 IEEE.

FocusFace: Multi-task Contrastive Learning for Masked Face Recognition

SARS-CoV-2 has presented direct and indirect challenges to the scientific community. One of the most prominent indirect challenges advents from the mandatory use of face masks in a large number of countries. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. It has been shown that the performance of these methods drops considerably in the presence of face masks, especially if the reference image is unmasked. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition. The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks. We also explore different approaches to design the contrastive learning module. Results are presented in terms of masked-masked (MM) and unmasked-masked (U-M) face verification performance. For both settings, the results are on par with published methods, but for M-M specifically, the proposed method was able to outperform all the solutions that it was compared to. We further show that when using our method on top of already existing methods the training computational costs decrease significantly while retaining similar performances. The implementation and the trained models are available at GitHub.

My Eyes Are Up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition

The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases. © 2021 Gesellschaft fur Informatik (GI). All rights reserved.

My Eyes Are up Here: Promoting Focus on Uncovered Regions in Masked Face Recognition

The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases. © 2021 IEEE.