Adaptive Learning Control and Monitoring of Oxygen Saturation for COVID-19 Patients

This paper proposes an adaptive learning control and monitoring of oxygen for patients with breathing complexities and respiratory diseases. By recording the oxygen saturation levels in real-time, this system uses an adaptive learning controller (ALC) to vary the oxygen delivered to the patient and maintain it in an optimum range. In the presented approach, the PID controller gain is tuned with the learning technique to provide improved response time and a proactive approach to oxygen control for the patient. A case study is performed by monitoring the time varying health vitals across different age groups to gain a better understanding of the relationship between these parameters for COVID-19 patients. This information is then used to improve the standard of care supplied to patients and reducing the time to recovery. Results show that ALC controlled the oxygen saturation within the target range of 90% to 94% SpO2, 77% and 80.1% of the time in patients aged 40 to 50-year-old and 50 to 60-year- old, respectively. It also had faster time to recovery to target SpO2 range when the concentration dropped rapidly or when the patient became hypoxic as compared to manual control of the oxygen saturation by the healthcare staff.