## 2 Critical Literature Review  
Due to the ever-increasing need for human supervision, the use of wearable devices has gained considerable ground for limiting COVID-19. Currently, large companies are investigating techniques to identify infected people and whether infected people are quarantined properly through wearable devices. Some of the projects that have been explicitly developed are listed below. A portable integration of IoT devices was implemented for monitoring body temperature, room temperature in real-time through a notification, and an alert system for quarantined people [16]. The alarm is transmitted when the body temperature exceeds the permissible temperature. In addition, an accelerometer algorithm was developed that predicts whether the quarantine's temperature rise is due to either immobility or normal movement and generates an output to infer whether the temperature is the result of physical exercise or not to adjust the temperature automatically. The experiment included an M5 stickC, a MEMS accelerometer, an infrared thermometer, and a digital temperature sensor. Indoor temperature and humidity are measures to limit the spread of the virus. The information is transmitted via WiFi and MQTT under normal conditions. When the WiFi signal is lost, Bluetooth transfers data from the isolated person, the electronic device of a family member, or a medical member. Evidence suggests that many sensors could be sued to develop fever monitoring applications, enable large-scale health monitoring, and provide high-temporal and accurate data on fever responses [17]. Findings from individuals, who reported COVID-19 infections, prove that relative increases in temperature are observable with portable devices and are associated with self-reported fever. Finally, the data support the hypothesis that disease prediction is possible using data continuously called by portable sensors. Subsequently, in [18], the authors proposed a framework consisting of three components: a lightweight and low-cost IoT node, a smartphone application, and fog-based Machine learning tools for data analysis and diagnosis. In this project, several health parameters were considered and monitored, including body temperature, cough rate, respiratory rate, and blood oxygen saturation. A dedicated smartphone app displays the condition of the user, and, if deemed necessary, a corresponding message notifies the user to maintain social distancing. Additionally, in this study, a Fuzzy Mamdani system (running at the fog server) computes the risk of spreading the infection regarding several environmental factors along with the health status of the user. Two distinct cases are distinguished for the communication between the IoT node and the fog server, specifically 4G/5G/Wi-Fi or LoRa, depending on environmental constraints. Lastly, in the context of this study, the authors presented a comparative analysis with respect to the energy requirements and bandwidth for multiple use-cases. Furthermore, a wearable monitoring device utilizing simple sensors and microcontrollers to track body temperature, heart rate, respiration rate, and other vital signs was presented in [19] in an attempt to detect COVID-19 symptoms. Additionally, in [20], the authors presented an affordable IoT-based application. This project's focal point was to minimize the probability of COVID-19 transfer. Specifically, a system to check patients' health status remotely was proposed, acquiring periodic measurements concerning the user's vital signs. The proposed design was composed of diverse set of sensors designated to measure the user's heart rate, blood oxygen level (MAX30100), and body temperature (MLX90164) connected to a microprocessor (ATmega 2560) by using the I2C protocol. Lastly, an IoT-based solution was suggested to conduct vital signs monitoring and alert officials in the event of detecting COVID-19 symptoms [21]. The proposed design entails the acquisition of readings from various sensors and determining the best course of action needed in each instance.