Classifying Chest X-Ray Images using Residual Neural Network

This unprecedented time of the COVID-19 outbreak challenged the status-quo whether it is on business operation, political leadership, scientific capability, engineering implementation, data analysis, and strategic thinking, in terms of resiliency, agility, and innovativeness. Due to some identified constraints, while addressing the issue of global health, human ingenuity has proven again that in times of crisis, it is our best asset. Constraints like limited testing capacity and lack of real-time information regarding the spread of the virus, are the highest priority in the mitigation process, aside from the development of vaccines and the pushing through of vaccination programs. Using the available Chest X-Ray Images dataset and an AI-Computer Vision Technique called Convolutional Neural Network, features of the images were extracted and classified as COVID-19 positive or not. This paper proposes the usage of the 18-layer Residual Neural Network (ResNet-18) as an architecture instead of other ResNet with a higher number of layers. The researcher achieves the highest validation accuracy of 99.26%. Moving forward, using this lower number of layers in training a model classifier, resolves the issue of device constraints such as storage capacity and computing resources while still assuring highly accurate outputs. © 2022 IEEE.

Social Distancing Monitoring and Contact Tracing on CCTV Footage

Social distancing is one of the most effective measures to prevent the spread of the COVID-19 disease. Most methods of enforcing this in the Philippines resort to manual methods. As such, a video-based social distancing monitoring tool can help ensure constant enforcement of social distancing due to the availability and up-time of CCTV cameras in various areas. This can be achieved by using object detection and tracking techniques. Object detection can be used to detect people within an area, and tracking can be used to watch people who get into close contact with one another. Contact tracing can also be performed by processing the social distancing measurements and tracking information. This information can be stored to keep a record of who has a high risk of infection based on who they came into contact with and for how long. We introduce a social distancing monitoring and contact tracing framework using the EfficientDet object detector and DeepSORT tracker. This framework is used to monitor social distancing violations and keep a record of violations associated to the tracked people. © 2022 IEEE.

Sentiment Analysis for COVID-19 Related Tweets Using Deep Bi-Directional Transformers

This study uses a pre-trained Bi-directional Encoder Representations from Transformers (BERT) with an AdamW optimizer for sentiment analysis of COVID-19 related tweets. This is performed on around 32,000 tweets from an annotated dataset of 190 million tweets. The sentiment of each tweet was predicted between three different classes, negative, neutral, and positive. Under sampling was performed to balance out the data and the model was fine-tuned over 4 epochs. The resulting model was best at predicting negative sentiment and worst at predicting neutral sentiment. The resulting accuracy was found to be 75.15%, however, increasing the amount of data used would likely improve this significantly. © 2022 IEEE.

Calibration and Testing of the Integrated Ventilator Scalar Measurement Module for a Bag-Valve-Mask-Based Emergency Ventilator

Open-source ventilators (OSVs) are considered as an immediate response for the shortages of ventilator equipment in hospitals due to the ongoing global pandemic caused by the Coronavirus disease 2019 (COVID-19). One of the designs explored for OSVs utilizes a bag-valve-mask as a source for mechanical ventilation. Despite its availability for use and being medically accepted, proper calibration must be observed in measuring ventilator scalars such as inspiratory pressure, inspiratory flow, and tidal volume to promote the safe use of the OSV and prevent OSV users to do more harm to the patient. This study discusses different calibration techniques to properly acquire ventilator scalar measurements using an integrated ventilator scalar measurement module. All in all, different calibration setups and bag-valve-mask-based mechanisms were tested and documented to determine an effective means to acquire accurate and precise ventilator scalar measurements.

Implementation of a Programmable Logic Controller (PLC)-Based Control System for a Bag-Valve-Mask-Based Emergency Ventilator

Automation is considered as the driving force of Fourth Industrial Revolution (Industry 4.0) to develop smart and automated devices for existing manufacturing processes. However, the global medical outbreak perpetrated by the Coronavirus Disease 2019 (COVID-19) challenged researchers to explore new concepts and innovate existing technologies whilst resolving the ongoing health crisis. Thus, the demand for utilizing the automation concept in biomedical devices is reasonably high. For this study, the researchers have successfully implemented an industrial-grade programmable logic controller that will control the mechanical ventilation process of a bag-valve-mask-based emergency ventilator. Various mechanisms were observed, and the results have been documented. © 2020 IEEE.

Design and Development of an Automated Compression Mechanism for a Bag-Valve-Mask-Based Emergency Ventilator

In response to COVID-19 pandemic, universities and related institutions around the world came up with various mechanical ventilator designs to help cope with the expected shortages of ventilators as the pandemic rages. Many of these designs are based on automating the manual operation of the Bag Valve Mask (BVM), a ubiquitous resuscitator device used for emergency ventilation or resuscitation of patients with breathing problems. In this paper, the mechanical design and development process for a BVM-based emergency ventilator is discussed. In particular, the evolution of the design from a simple, low-cost device to a more sophisticated system acceptable to pulmonologists and related medical practitioners is documented. © 2020 IEEE.