| dc.description.abstract | One of the most significant threats associated with COVID-19 is the increase in hospital admission. The primary study gathering the data was integral as the basis of this thesis. The study investigates whether using biosensors to monitor patients infected by COVID-19 will impact outcomes compared to patients who are not monitored electronically. The biosensors are used to monitor vitals such as heart rate, respiration rate, heart rate variability, and relative stroke volume of a patient group at home.
An interactive and user-friendly GUI was developed to visualize data from patients that participate in the study. The user can navigate the plot and change settings from a menu bar. A vital functionality implemented is the ’Extract data’ function. By extracting data, health care professionals can use the data for further data analysis. Data analysis was
performed to test the ’Extract Data’ functionality. The analysis compared the respiration rate of two patients by calculating the average, median, and standard deviation. The results show that patient 1 has a higher average and median, while patient 2 has a higher standard deviation.
The GUI is implemented using Python and Qt as a framework; this has been used because it is open-source and easy to install or import. Python is an object-oriented language with clear syntax. | |
