Machine and Deep Learning Algorithms, Computer Vision Technologies, and Internet of Things-based Healthcare Monitoring Systems in COVID-19 Prevention, Testing, Detection, and Treatment

Abstract

Keywords: Internet of Things;machine and deep learning algorithm;COVID-19 1.Introduction The purpose of our systematic review is to examine the recently published literature on COVID-19 prevention, testing, detection, and treatment, and integrate the insights it configures on machine and deep learning algorithms, computer vision technologies, and Internet of Things-based healthcare monitoring systems. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), COVID19 detection and diagnostic tools (section 4), machine learning techniques, healthcare sensor devices, and computer vision (section 5), machine learning algorithms and Internet of Things-based monitoring systems (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 3) 5.Machine Learning Techniques, Healthcare Sensor Devices, and Computer Vision Internet of Things-based healthcare monitoring systems are pivotal in accurate and suitable patient treatment (Jain et al., 2021;Li et al., 2021;Rhayem et al., 2021;Zhang et al., 2021a) by integrating medical wearable sensors, actuators, and networked devices. (Table 4) 6.Machine Learning Algorithms and Internet of Things-based Monitoring Systems Internet of Medical Things devices and wearables can be pivotal in contact tracing, early diagnosis, and symptom tracking (Khowaja et al., 2021;Mehrdad et al., 2021;Tai et al., 2021) by use of machine learning techniques, neural network architectures, and data fusion.