The Psychological Impact of the COVID-19 Pandemic on Frontline Healthcare Workers. A Systematic Review and a Meta-Analysis

Introduction: The COVID-19 pandemic has created a chronically stressful work environment for healthcare workers, increasing the negative psychological effects experienced. Aim(s): The authors of this systematic review and meta-analysis aimed to assess the impact of COVID-19 on frontline healthcare workers' mental health, using various psychological outcomes. Method(s): A systematic literature search was conducted up until June 30th, 2022 on MEDLINE, EMBASE, CINAHL, Cochrane Library, Web of Science, ClinicalTrials.gov, and Dissertations and Theses. Result(s): This meta-analysis includes 22 cross-sectional studies with a total of 32,690 participants. Anxiety (ES = 0.23, CI: [0.18, 0.28]), depression (ES = 0.17, CI: [0.10, 0.24]), PTSD (ES = 0.28, CI: [0.08, 0.48]), and stress (ES = 0.35, CI: [0.17, 0.53]) was significantly prevalent among frontline healthcare workers. Conclusion(s): Our results suggested that European healthcare workers were experiencing high psychological symptoms associated with the COVID-19 pandemic. The monitoring of their psychological symptoms, preventative interventions, and treatments should be implemented to prevent, reduce, and treat the worsening of their mental health.Copyright © 2023 The Authors. Published by Semmelweis University, Institute of Mental Health, Budapest.

A Remote Rehabilitation and Evaluation System Based on Azure Kinect

In response to the shortage, uneven distribution, and high cost of rehabilitation resources in the context of the COVID-19 pandemic, we developed a low-cost, easy-to-use remote rehabilitation system that allows patients to perform rehabilitation training and receive real-time guidance from doctors at home. The proposed system uses Azure Kinect to capture motions with an error of just 3% compared to professional motion capture systems. In addition, the system pro-vides an automatic evaluation function of rehabilitation training, including evaluation of motion angles and trajectories. After acquiring the user's 3D mo-tions, the system synchronizes the 3D motions to the virtual human body model in Unity with an average error of less than 1%, which gives the user a more intuitive and interactive experience. After a series of evaluation experiments, we verified the usability, con-venience, and high accuracy of the system, finally con-cluding that the system can be used in practical rehabilitation applications. © Fuji Technology Press Ltd.

Kinect-based 3D Human Motion Acquisition and Evaluation System for Remote Rehabilitation and Exercise

With the increasingly serious aging situation, more and more elderly people are physically disabled. In addition, the current rehabilitation resources have the problems of shortage and uneven distribution, coupled with the impact of COVID-19 in early 2019, most patients have been greatly restricted from going to the rehabilitation center for training. To solve these problems, we propose a "Kinect-based 3D Human Motion Acquisition and Evaluation System for Remote Rehabilitation and Exercise"which uses the Kinect3 camera to obtain human motion with an error rate of only 3% when the body is in front of the camera. Then we use Unity to create a humanoid virtual model and interactive scene and synchronize the real body motion to the virtual model with an average error less than 1%. At the same time, our system provides reliable and highly accurate methods for evaluating actions based on angles and trajectories. What's more, users don't need to wear any wearable devices when using the system. It is a mark-less motion acquisition system, which reduces the cost and improves the usability and scalability of the system. And the interactive virtual scenes also increase the training motivation of users. © 2022 IEEE.

COVID-19 Face Mask Restoration Using Pix2pix model

Accurate facial recognition can effectively help the population combat the disease by offering risk-free phone usage, access controls, etc. In the era of COVID-19, a mask has become a necessity. However, masks may reduce the accuracy of face recognition to some degree. Thus, it is necessary to use deep learning to increase face recognition accuracy by recovering the face with a mask. For this purpose, this study proposed an AI-based model based on Pix2pix and U-net generator for restoring face mask images using the paired image database. In the training step, we used two adversarial models, including one generator and one discriminator. Then they are extended to a conditional model, which will be piped to the Pix2pix algorithm once again. U-Net was built in the training of the generator. The loss curves of generator and discriminators show that as iteration time increases, the loss of fake discriminator becomes lower stably. In contrast, the loss of real discriminator has the same tendency. In the meantime, the loss of generator shows an increased tendency. The result indicates that our model can help build reliable face mask restoration for daily use, which helps to improve the recognition accuracy of the face with a mask.