# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 2 Related Works
These signs are regarded as the main symptoms of COVID-19 infection, with a temperature greater than 38 \({}^{\circ}\)C, heart rate measured at 100 bpm, and oxygen saturation as 92-96% indicating possible virus infection. A built-in microphone was also implemented to capture samples of cough sounds which are then processed through an AI model to aid in the monitoring of potentially infected patients. An automated health care system such as this can greatly reduce stress and provide a means of communication between doctors, medical authorities, and family respondents, and serve as a tool to collect and analyze the data needed to monitor and control the social life of patients and manage them during the pandemic era. Additionally, [18] surveyed edge IoT in healthcare. Edge intelligence, which is a combination of AI and edge computing that targets health data classification by tracking and identifying vital signs using state-of-the-art deep learning (DL) techniques, was examined. A comprehensive analysis of the use of cutting-edge AI-based classification and prediction techniques employed for edge intelligence, as well as its many advantages, was carried out. The study also offered a brief overview of the general usage of IoT solutions in edge platforms for medical treatment and healthcare. The study considered research on physiological health data analysis, edge-based IoT systems for rehabilitation, skin disease detection, and diet monitoring, epidemic prevention systems, and research studies on diabetes treatments. Machine learning (ML), as well as big data and blockchain in IoT-based healthcare frameworks, was also studied. In another comprehensive review, [19] introduced the concept of the Internet of Health Things (IoHT) and its application for intelligent vital health signs monitoring in hospital wards. The suggested concept focuses on surveying the different approaches that could be applied to gathering and combining vital signs data in hospitals. Some common heuristic approaches, such as weighted early warning scoring systems and the possibility of employing intelligent algorithms, were considered. Different vital signs were also discussed, including BP, BT, HR, RR, SPO2, and urine output. It was reported that the first five physiological parameters mentioned were the common parameters measured in the human body. It is believed that the development and general adoption of the IoHT concept for the analysis of physiological parameters will lead to the possibility of predicting imminent and critical problems associated with humans, making it easy for caregivers to analyze vital health signs even via remote locations. Similarly, [7] presented a survey on the IoT and cloud computing for healthcare. The survey considered how disruptive technologies such as cloud computing, ambient assisted living, big data, and wearables are being applied to solve various issues in the healthcare industry. Various IoT, health regulations, and policies were also analyzed to determine how well they promote the sustainable development of IoT and cloud computing in the healthcare industry. Specifically, an in-depth review of IoT privacy and security issues, such as potential threats, attack types, and security setups, was carried out from a healthcare viewpoint, and an analysis of previous well-known security models was also conducted to deal with security risks and provide trends and highlight opportunities, as well as challenges for the future development of IoT-based health care [20] conducted a comprehensive review of critical healthcare and specific vital signs of patient monitoring. Several clinical issues to consider when measuring vital signs were described. They argued that medical personnel have relied on five vital signs (BT, HR, BP, RR, and SPO2) to assess the health condition of their patients. However, due to the advancement in healthcare, these vital signs may not be adequate to identify those with health issues.