Internet of Wearable Medical Things for COVID-19 Diagnostics

Wearable devices have played a key role in the medical industry, especially since the COVID-19 pandemic spread. The need for a self-monitoring system increased since the spread of the virus. With the development of semiconductor technology and the increased research and development in medical wearable devices, wearable devices have been able to detect the medical condition of patients. This paper presents a biomedical wearable device to monitor the vital signs of patients. The device can be used to detect the patient COVID-19 infection. Data were extracted using different sensors and other components, and results were displayed on a mobile application that showed the health status of the patient. A PCB (Printed Circuit Board) design was made for the purpose of making the system a wearable device. The system power consumption ranged from 5-37.5mW. © 2022 IEEE.

Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization

This paper presents an efficient interval type 2 fuzzy (IT2F) based on a single neuron proportional–integral–derivative (PID), also known as IT2FSNPID controller. The main purpose of the proposed control technique is to track the motion profile of the brushless DC (BLDC) motor. Also, a comparative study was investigated fuzzy type 1 (FT1) and IT2F. IT2F can treat the uncertainty and nonlinearity of the BLDC motor drive electric system in contrast to FT1. The parameters of each control technique were obtained using a new COVID-19 optimization algorithm according to an objective function. Moreover, several tests had been performed to ensure the ability of fuzzy type to absorb the system uncertainty and nonlinearity. All controllers were utilized to operate the BLDC motor sudden change in load and continuous load. The simulation results show that the IT2FSNPID can improve the dynamic response of linear and nonlinear of the same BLDC motor and accommodate the system uncertainty significantly. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

Face Masked and Unmasked Humans Detection and Tracking in Video Surveillance

Due to the spread of COVID-19, people wearing face masks became a regular occurrence worldwide. Moreover, there are nations where covering one's face is done for religious or cultural reasons, or even wear face masks for convenience. However, current face detection and tracking systems are hindered by face masks as the full facial features are no longer visible and therefore became less effective. In this paper, it is proposed to improve current face detection and long-term tracking technology by extracting the facial features of the top regions of the face, taking into account the eye, eyebrow, and forehead. The methodology contains two models, the face detector and the long-term object tracker. The face detection model uses a joint dataset from ISL-UFMD and MaskedFace-Net. The dataset is used to train a Keras sequential model. The object detection model uses pre-trained YOLOv4 weights and DeepSORT to identify people and uses the tracking-by-detection method to perform long-term tracking throughout the surveillance video. The final face detection model results show a testing accuracy of 93.33% and a loss of 26.92%, which are up to par and comparable with other state-of-the-art models. © 2022 IEEE.