Low Cost System on IOT Technology, for remote control of Biomedical Equipment

The effects of the pandemic produced by COVID-19 have caused many changes in the protocols of care and operation of medical equipment, due to the need to be able to have less contact with the patient, while ensuring patient care, In the present work, a solution is implemented to be able to control medical equipment remotely, avoiding contact with patients to a minimum, the proposed methodology makes use of a power outlet that has the ability to connect to a wireless network and thus allow the on and off by means of an application installed on any mobile device, the results demonstrate the applicability in various equipment, as well as being able to work in many connections, each equipment connected to a specific power outlet. One of the necessary recommendations is security in the wireless network, because no unauthorized user can access the network and be able to connect, in this way you have to have a specific wireless network for the use of these devices, the proposal is Applicable and scalable according to the needs and uses that may be given to it. © 2022 IEEE.

Towards a Low-cost and Mobile System for Quarantine Tracking during Pandemics

In recent years, the coronavirus pandemic has caused and continues to cause a large number of deaths today. Governments all over the world have taken measures to reduce the spread of the virus. Home quarantine was proven to be a successful way to control and mitigate the spread of COVID-19. The current methods of tracking home-quarantined people use tracking bracelets or mobile applications, which are still in their early development stage. In this paper, we propose a quarantine tracking system to track users during their quarantine period. The system consists of a web portal designed for the concerned authorities and the quarantined user's mobile application. The proposed application randomly prompts users to verify their quarantine by uploading their selfies. We verify the user's quarantine status through an automatic location and selfie verification. Our system provides a specific set of features that can be set by the concerned authorities, such as the frequency of prompts and the allowed response time. Violation reports can also be accessed by the concerned authorities to keep track of quarantined users' violations. Our results validate the usefulness and potential of our system as a quarantine tracking tool. It also opens the door for further development, allowing governments to handle any upcoming pandemics. © 2021 IEEE.

A Low-Cost Control Occupancy Solution Using a Time-of-Flight Ranging Sensor Laser

The pandemic situation has driven to several measures to prevent the spread of COVID-19. One of these measures is social distance and, as a consequence, limitation of capacity of indoor closed spaces. This makes necessary the deployment of systems that help to control occupancy of spaces. This work proposes a low-cost system to control access to an indoor closed space with a single door. The system is based in a two laser Time-of-Flight sensors VL53L0X over a HiLetgo UNO R3D1R32 ESP32 micro-controller. The system counts the occupancy of the room and share it with a database and a dashboard, using Node-RED. The tested prototype shows a 86.6% reliability that increases to a 100% reliability when users are informed to enter or exit one by one. The main contributions of this work are: to control capacity of one-entrance indoor closed space with a low cost open system;and to record occupancy of the room in order to analyse it behaviour with time. © 2021 Copyright for this paper by its authors.

An Effective approach for implementing COVID-19 CT Lung Segmentation using Low-Cost System on Chip

Computer vision techniques always had played a salient role in numerous medical fields, especially in image diagnosis. Amidst a global pandemic situation, one of the archetypal methods assisting healthcare professionals in diagnosing various types of lung cancers, heart diseases, and COVID-19 infection is the Computed Tomography (CT) medical imaging technique. Segmentation of Lung and Infection with high accuracy in COVID-19 CT scans can play a vital role in the prognosis and diagnosis of a mass population of infected patients. Most of the existing works are predominately based on large private data sets that are practically impossible to obtain during a pandemic situation. Moreover, it is difficult to compare the segmentation methods as the data set are obtained in various geographical areas and developed and implemented in different environments. To help the current global pandemic situation, we are proposing a highly data-efficient method that gets trained on 20 expert annotated COVID-19 cases. To increase the efficiency rate further, the proposed model has been implemented on NVIDIA-Jetson Nano (System-on-Chip) to completely exploit the GPU performance for a medical application machine learning module. To compare the results, we tested the performance with conventional U-Net architecture and calculated the performance metrics. The proposed state-of-art method proves better than the conventional architecture delivering a Dice Similarity Coefficient of 99%. © 2021 IEEE.