Remote Patient Monitoring Systems, Wearable Internet of Medical Things Sensor Devices, and Deep Learning-based Computer Vision Algorithms in COVID-19 Screening, Detection, Diagnosis, and Treatment

Abstract

Based on an in-depth survey of the literature, the purpose of the paper is to explore remote patient monitoring systems, wearable Internet of Medical Things sensor devices, and deep learning-based computer vision algorithms in COVID-19 screening, detection, diagnosis, and treatment. Keywords: remote patient monitoring;Internet of Medical Things;COVID-19 1.Introduction The purpose of my systematic review is to examine the recently published literature on COVID-19 screening, detection, diagnosis, and treatment, and integrate the insights it configures on remote patient monitoring systems, wearable Internet of Medical Things sensor devices, and deep learning-based computer vision algorithms. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), machine and deep learning-based COVID-19 diagnostic and predicting tools and applications (section 4), wearable Internet of Medical Things devices and sensing technologies (section 5), machine learning algorithms, implantable medical devices, wireless body networks, and computer vision (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10). (Table 4) 6.Machine Learning Algorithms, Implantable Medical Devices, Wireless Body Networks, and Computer Vision Internet of Medical Things can be instrumental in COVID-19 prevention and detection accuracy (Douglas Miller and Brown, 2019;Kong et al., 2021;Li et al., 2021;Rhayem et al., 2021) through data collection and processing, healthcare monitoring systems, and intervention measures.