## 4 System Description  
### General Approach  
Recently, the advent of IoT technologies has redefined health care analytics. Abnormal measurements play a prominent role in healthcare analytics [27, 28]. An IoT node transmits the data through a Bluetooth connection. At that moment, a doctor or a person responsible for the patient can observe the temperature in correlation with the activity and judge the health situation.  
Figure 2: Principle of operation of the designed system.  
Wearable activity trackers can provide unique insights and contribute to the early detection of COVID-19 cases [29] by monitoring patients using wearable devices. The ideal combination can be achieved with the wearable device and the usage of IoT for the health monitoring of people in isolation [30]. This study provided an activity recognition tool using a 9-axis IMU, which allows the variation of signals to reflect the human activity. The approach focuses on distinguishing between regular activity, so the CDA is proved effective.  
Using several temperature sensors allowed us to track the temperature fluctuations because fever is one of the key symptoms of COVID-19. The contact thermometer was configured appropriately to monitor the user's body temperature with great fidelity. It is deemed worthy to mention that in the beginning of the monitoring, the sensor required a few seconds to calibrate. The standard body temperature threshold was automatically adapted depending on the physical exercise status. An infrared thermometer is an indispensable sensor that quickly finds the users' temperature but lacks precision [31]. This type of recognition allows the observer to keep track of the users' temperature variations and find out if the temperature is due to physical exercise [32].  
On the contrary, the ambient sensor HTS221--which was embedded in the board--responsible for measuring room temperature and humidity is necessary to assure the quarantined person's suitable living conditions and restrain COVID-19 [33]. Therefore, in our design, there was a combination of three temperature sensors, each one checking the different types of temperature (touch/I.R./ambient) and humidity, as well as continuously checking the AmT (Ambient Temperature) and the AmH (Ambient temperature) surrounding the user. All the abovementioned data were shown to the user via the OLED display. Tracking of the ambient parameters is also important because if one is under supervision, it is mandatory to adjust the environment variables to halt the spread of the virus, in the particular values between 20-60% of humidity and 20-24 \({}^{\circ}\)C concerning the ambient temperature [34].  
By analyzing the data measurements between room conditions and body state, we could improve the monitoring capabilities of infected people without needing to access large databases and without large training datasets. Besides that, we then send warnings via the OLED display and the BLE communicate when the body temperature exceeds the allowed threshold. The importance of the proposed system is that it combines simple logic, a small size, and multiple functionalities. This system features upgradeability, utilizing machine learning models. It can be customized or progressed to a more complex and multipurpose system that uses the rest of the sensors embedded on the Arduino Nano 33 BLE sense board.