## 6 Conclusions  
IoT-enables remote monitoring in the healthcare sector and enables physicians to treat their patients. Furthermore, remote monitoring of patients' health contributes to curtail their hospital stay and prevents re-admissions. Therefore, the profound role of IoT in healthcare-related implementations becomes evident.  
In the present study, a wearable system that can be used to monitor COVID-19 infected health status was presented. It was shown that the designed system successfully managed to monitor the user temperature and the ambient room temperature and humidity. The numerous measurements were acknowledged from the self-isolated person due to COVID-19. Furthermore, IoT enabled connecting sensor data supervising users via BLE. Utilizing the wireless protocol, the information almost always reached the connected devices, which is critical since the virus is most infectious via the respiratory system. This project's primary goal was to design a functional device while also bearing its intuitiveness, practicality, along with its affordability. This small-size device was equipped with sensors managing to supervise the user's health and distinguish high temperature due to fever or physical exercise. This device successfully measured wrist temperature at all states, ranging from 32 \({}^{\circ}\)C initially to 39 \({}^{\circ}\)C, providing better battery autonomy than other similar devices from pertinent literature, lasting over 12 h of usage and data recording, with fast charging capabilities (500 mA), and utilizing the BLE 5.0 protocol for data wireless data transmission and low power consumption.  
This study emphasized the development of a Continuous Displacement Algorithm following the default thresholds of maximum points and temperature. Besides that, the ambient temperature and humidity were always available for the quarantined person to use and adjust accordingly to ensure suitable living conditions. Lastly, our samples were tested in deep learning analysis. The samples acquired during the three measurement periods were gathered and fed to a unidimensional CNN. This practice showcased remarkable accuracy in classifying the condition of the user concerning its febriility, achieving a perfect classification accuracy of the samples in the test set. Currently, the designed device underwent a series of clinical trials with the support of infected people to validate its strengths and identify its weaknesses. Besides, machine learning techniques to improve the system's accuracy were considered.  
Henceforward, the designed device will be tested with more people to validate its strengths and identify its weaknesses. In addition to that, it is scheduled to use machine learning techniques to improve the system's accuracy. Another aspect providing room for improvement is the battery life along with the addition of a pulse oximeter sensor. This device can also be upgraded in terms of sensor calibration to provide more accurate results from all the sensors included. We also aim to materialize further improvements  
Figure 13: ROC curve of the 1D CNN classifier.  
regarding the following attributes: the CDA, and the addition of a WiFi and GPS module for selection, MQTT protocol for data visualization on a personal computer or a mobile device interactively via NodeRed, and an OLED screen from non-touch to touch display. Furthermore, one future direction entails experimentation of this device and its efficacy on actual patients suffering from COVID-19.  
Conceptualization, C.S.; Data curation, A.-C.D., P.T. and N.V.; Formal analysis, A.-C.D., P.T. and C.S.; Project administration, C.S. and N.V.; Software, A.-C.D. and P.T.; Supervision, C.S. and N.V.; Validation, C.S. and N.V.; Writing--original draft, A.-C.D. and P.T.; Writing--review & editing, C.S. and N.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.