Adaptive Learning Control and Monitoring of Oxygen Saturation for

COVID-19 Patients

Lubna Farhi, Rija Rehman and Muhammad Ammar Khan

Department of Electronic Engineering, Sir Syed University of Engineering and Technology, Karachi, Pakistan

Keywords: Oxygen Saturation, Adaptive Learning Controller, PID Controller, COVID-19.

Abstract: 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.

1 INTRODUCTION

The start of the year 2020 introduced the globe into

an unprecedented time of biological turmoil, the likes

of which has not been seen since the black plague.

SARS-COV-2 is a strain of virus that once infects a

patient, results in the disease known as COVID-19

(WHO coronavirus-2019/technical, 2019). COVID-

19 was declared a global pandemic by the World

Health Organization (WHO) on 11th March 2020

(WHO coronavirus-2019/events, 2019). As of

November 2020, approximately 54 million people

have been infected by this virus in the world, out of

which 1.3 million people have died

(worldometers.info, 2019). Meanwhile, roughly

350,000 people have been afflicted in Pakistan,

amongst which approximately 7,000 have passed

away from the SARS-COV-2 virus

(worldometers/Pakistan, 2020).

The reason why COVID-19 is considered so

threatening is because currently there are no available

vaccines that can provide protection against the strain

of virus that causes this disease. It is also highly

infectious and affects the lungs thereby causing

Severe Acute Respiratory failure. Once a person is

infected, they experience various symptoms amongst

which the prominent ones are loss of taste sensation,

high sustained fever, and difficulty in breathing.

However, out of all of the aforementioned symptoms,

the latter is the most problematic as it can lead to the

patient experiencing acute hypoxemic respiratory

failure or chronic respiratory failure. Due to the lack

of antibody vaccines and such deadly symptoms, the

National Institute of Health (NIH), World Health

Organization (WHO) and Centre for Disease Control

(CDC) have outlined supportive care guidelines

where healthcare providers are required to observe

the patient under isolation and provide necessary care

to relieve the symptoms as much as possible through

pain medication, rest and adequate food supplement.

By monitoring a patient’s health vitals such as

oxygen saturation (SpO2), body temperature, pulse

rate and blood pressure, health care facilities may be

able to determine the progress of a patient’s recovery.

Body temperature is noted to observe the state of

fever, while pulse rate and blood pressure are

monitored to ensure that the patient is not having

trouble breathing. Lastly, SpO2 is necessary to

monitor to ensure that the patient does not become

hypoxemic and that the lungs are functioning

properly. Oxygen saturation (SpO2) mentions to the

volume of oxygen that is in blood. The body needs an

explicit amount of oxygen in blood to function

appropriately. Oxygen consumption within the body

184

Farhi, L., Rehman, R. and Khan, M.

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

DOI: 10.5220/0010381701840190

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 3: BIOINFORMATICS, pages 184-190

ISBN: 978-989-758-490-9

is Oxygen consumption = (Arterial Oxygen-Venous

Oxygen) * Blood flow. The oxygen-haemoglobin

dissociation is a function of the partial pressure of

oxygen (PO2). Haemoglobin will be 100% saturated

with oxygen if PO2 =100 mmHg “Each gram of

haemoglobin is capable of carrying 1.34 mL of

oxygen. The solubility coefficient of oxygen in

plasma is 0.003. This coefficient represents the

volume of oxygen in mL that will dissolve in 100mL

of plasma for each 1 mmHg increment in the

PO2.” Oxygen Content = (0.003 × PO2) + (1.34 ×

Haemoglobin × Oxygen Saturation) (Kaufman,

2020).

Currently, health care providers monitor SpO2

and control the supply of oxygen to critical care

patients by manually adjusting the supply of oxygen

from the cylinder or source. This is not only

inefficient, but is also risky, prone to error and in

cases of a high number of patients can lead to

overloading of the staff and healthcare system.

Therefore, to reduce the burden on the healthcare

system and facilitate quicker recovery this

methodology was proposed, which utilizes an

adaptive learning controller that would monitor and

change the oxygen saturation for patients hospitalised

with COVID-19.

Automated systems have been previously shown

to have better outcomes on patients as compared to

manually controlled systems. This was demonstrated

by Alexander et. al. in their study (Alexandre, 2020).

They proved that when a post-surgery patient’s

anaesthesia is automatically controlled, they not only

recover quicker but also have fewer post-surgery

complications as compared to manual anaesthesia

delivery control. In addition, the benefits of the use of

automated systems to continuously monitor health

vitals of recovering patients was discussed by

(Appelboom et al, 2014) in their paper. They

proposed and demonstrated that wearable technology

can improve the quality of supportive care through

continuous monitoring of vitals. These vitals can then

be reported to health professionals who will have a

more detailed history of their patient resulting in a

well-defined and succinct care plan. Furthermore,

Kaushal et al highlighted the benefits of using

automated technology for healthcare in their study

(Kaushal, 2002). They analysed the impact of

information technology and automation on the full

spectrum of healthcare delivery – from diagnosis to

post-operative care and concluded that IT integration

into healthcare systems not only reduce

complications but also reduce the burden on the

healthcare staff. Similarly, by James et al showed that

through automation intervention, medical staff’s

workload can be drastically reduced resulting in

fewer errors and improved work-life balance (James,

2013).

In this paper we are taking the same approach as

the previously mentioned research papers and are

conducting a study regarding the efficacy of

automated oxygen monitoring and saturation-control

for COVID-19 patients. An adaptive learning control

system will be utilized to monitor and control the vital

signs i.e. SpO2 and pulse rate and temperature of

COVID-19 patients requiring critical care. In such

scenarios where patients’ condition is rapidly

changing in response to the medical treatment or

ventilation supportive care, it is risky as well as time

consuming for hospital staff to continuously monitor

their progress. Moreover, a rapid increase in COVID-

19 cases is also leading to overloading the systems

and staff leading to a reduction in the quality of

supportive care. An adaptive control model could

make the monitoring of vital signs more efficient and

accurate for staff, while also keeping in consideration

the SOPs for COVID-19. This approach could

ultimately improve the recovery time of patients

thereby reducing the load on hospitals.

2 METHODOLOGY

The approach to the proposed methodology was

twofold – develop a robust control system and

integrate it with health vitals. This required that the

adaptive learning controller not only have accurate

and reliable control, but it must also be able to intake

continuous variable oxygen data and appropriately

adjust the output in real-time.

Since the controller is

responsible for adjusting a sensitive parameter that

has a direct impact on the patient’s health, it must

have the capability for minute adjustments while also

being able to learn the oxygen variation to minimize

errors. The following sections explain how the

controller was developed and combined to monitor

and adjust oxygen in real-time.

2.1 Adaptive Learning Controller

(ALC)

To achieve the precise results, input must control the

optimized values of gain of PID controller. Noise

disturbances influence that are not modelled, make it

complex to maintain the PID control gains by

𝜃 = 𝐾



𝐾



𝐾



 at optimal values throughout. It

may turn into a serious issue to sustain the quality of

controller, to solve this issue, an adaptive learning

PID controller has proposed that enhance the

controller performance and improved accuracy due to

its memory feature. In PID controller 𝜇 , 𝑦 and 𝑒

denote the control input, output and error signal,

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

185

conventional control of PID can be express as

follows:

𝜇





𝑖



= 𝜇





𝑖



𝐾



𝑒





𝑖1



𝐾



𝑒



𝑛





𝐾





𝑒





𝑖1



 𝑒





𝑖





,𝑖 ∈0,𝑁1

(1)

Figure 1: Adaptive learning controller.

Where,

𝑒





𝑖



=𝑦





𝑖



𝑦





𝑖



, 𝑖 ∈



0, 𝑁  1



(2)

Applying (1) in the initial trial, showed that the

control input is similar as in the PID controller. In the

second trial of actual system, responses were not

according to the system output values, hence error

was integrated with the second input of the system.

This is the change analysed between output value

𝑦





𝑖



and actual system output in the initial trial, in

this way (1).

Proposed adaptive learning controller generated

control input in this manner just after the second trial.

So, the suggested learning control system can be

expressed by,

𝜇





𝑖



= 𝜇





𝑖



𝐾



𝑒





𝑖1



𝐾



𝑒



𝑛





𝐾





𝑒





𝑖1



𝑒





𝑖





𝑤ℎ𝑒𝑟𝑒 𝑖 ∈ 0,𝑁  1

(3)

𝑒





𝑖



= 𝑦





𝑖



𝑦





𝑖



(4)

This can be clearly seen in Figure 1. Having learning

operation based on the previous states, it is expected

to achieve the stable enhanced control results due to

the learning based control technique.

2.1.1 ALC using Recursive Least Square

(RLS) Algorithm

The adaptation mechanism is as follows. After the

detection of some error between standard and

measured SpO2, Controller response has decayed the

transient period. PID controller parameter vector to

be tuned in the controller is by 𝜃 = 𝐾



𝐾



𝐾



in

eq. (1). In eq. (4) where y

is the closed-loop response

under the controller parameters y

is the actual time

response of the controlled system.

Based on the RLS algorithms, we tune the

parameters 𝜃 which are the PID gain values so that

the following performance index J is minimized

=𝑦





𝑖



𝑦





𝑖









(5)

Where N is the number of time-response samples.

RLS is an algorithm which recursively finds the

optimal estimate (𝑘) of the controller parameter by

using 𝜃(𝑘−1)

2.2 Oxygen Control and Deliverance

Oxygen saturation (SpO2) is monitored via an

oximeter designed to take reading with a sampling

rate of 500 Hz (reading taken every 2ms). The

oximeter utilizes an IR LED and a photodiode that are

difference between the actual concentration and the

desired oxygen saturation levels. These decisions are

based on the information shown in Table 1. Below a

SpO2 of 85%, the patient is hypoxic and requires

immediate attention from the healthcare staff. For this

reason, the controller is tasked to sound an alarm, call

emergency, and maximize the oxygen output to the

patient to ensure that the lungs are getting enough

oxygen. Between 85% and 90% oxygen saturation

levels, the patient is considered to be on the cusp of

critical attention which is why an attendant is required

to be on-site while the controller maintains maximum

oxygen output. Once the SpO2 levels have reached

90% to 94%, then the oxygen is said to have been in

a safe range where conservative oxygenation shall be

employed. In this case, oxygen delivery shall be

gradually reduced such that the SpO2 level is

maintained at 94% or greater than 94% which is the

target (

Hansen, 2018)

Table 1: Controller parameters 𝜃 = 𝐾



𝐾



𝐾



 by

adjustment of Oxygen regulator to attain the Saturated

Levels.

Oxygen Saturated Levels (Health line, 2019)

SpO₂<85% 85% < SpO₂ ≤ 90% 90% < SpO₂ < 94% SpO₂= target

Call

emergency,

sound alarm

and Maximize

oxygen output.

Max. Parameters

Adjustment,

attendant presence

required.

Min. parameters

Adjustment

Maintain

parameters

BIOINFORMATICS 2021 - 12th International Conference on Bioinformatics Models, Methods and Algorithms

186

2.3 Process Flow Diagram

Figure 2 represents the overall integration of the

adaptive learning controller with the oxygen

deliverance and monitoring system. Upon receiving a

reading from the input sensor (oximeter) from the

patient, the controller calculates an error value E. This

error is determined by comparing the patient’s SpO2

levels with the standard required (minimum of 94%

oxygen saturation) in a normal patient. Then, on the

basis of this error value the adaptive learning

controller provides the oxygen delivery by adjusting

its controller gains. This leads to a change in the SpO2

levels of the patient which are then used to calculate

the error again and adjust the oxygen delivery until

this iterative process results in an error of E=0. This

signifies that the patient’s oxygen saturation is above

94% and they are stable at which point the controller

maintains its settings to provide a constant supply

oxygen.

Figure 2: Flow diagram of adaptive learning control and

monitoring of Oxygen saturation.

Adaptive Learning (ALC) controller is using the

recursive least square (RLS) algorithm. RLS

algorithm is used to update the PID gains in real time

(as system operates) to force the actual system to

behave like a desired reference model.

It shows that the adaptive learning controller

adjusts the PID parameters i.e. gains of PID controller

Kp, Ki and Kd of oxygen regulator to attain the

saturated Levels. The error generated is

proportionally related to the variation of SpO2 from

the target value. In case, SpO₂ < 85% or 85% < SpO₂

≤ 90% the amount of error generated is large which

causes the Kp to increase thus increasing the

regulating speed of the motor to provide a faster

response. Additionally, Kd increases to its maximum

value to reduce overshoot and maintain the speed of

regulating motor. Lastly, Ki increases to reduce

steady state error and control the overshoot to

maintain the stability of valve control and

correspondingly, oxygen levels.

For oxygen saturation level 90% < SpO₂ < 94%

minimum gain adjustments will be required and

similarly when error is zero and SpO2 = target level

then gains of PID controller will be sustained on their

existing values.

3 EXPERIMENTAL SETUP

The designed circuit provides a new and improved

respiration system which automatically regulates the

fractional inspired oxygen to a patient.

The system hardware consists of a

Microcontroller (Arduino Uno), Pulse

oximetry sensor (Pulse oximeter max 30100), LCD,

servo motor, keyboard and other components (sound

indication and LED indication). The hardware setup

of oxygen control is illustrated in Figure 3.

Figure 3: Circuit diagram and measured signal with sound

and LED indication. Alarm is triggered when SpO₂ < 85%.

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

187

Pulse oximetry sensor measures the oxygen

saturation of a patient's blood. This device consists of

a red and an infrared light source, photo detectors, and

a probe to transmit light through a translucent,

pulsating arterial bed, typically a fingertip or

earlobe that uses 5V/3.3V serial communication. The

dissolved oxygen measurement is triggered by

receiving a measurement via the RX port of the

Arduino while the motor control is provided by the

TX port. The sensor echoes the command and

appends the measured oxygen concentration. If the

measured oxygen concentration is below a certain

threshold i.e. SpO₂ < 94% a valve is opened which

will supply additional oxygen to the patient through a

connected oxygen supply.

4 RESULT AND ANALYSIS

In the presented approach, a comparison between

automatic and manual control was used to

demonstrate the efficacy of adaptive learning

controller for oxygen concentration in COVID-19

patients. It was observed that the automatic mode via

ALC control was the better option as it allowed the

patients, for all age groups, to recover in less amount

of time. The automatic mode also took a conservative

oxygenation approach where only enough oxygen

was provided to bring the patient back to 92% - 96%

SpO2. This approach has been proven to be a better

option towards the needs of patients suffering from

Acute Respiratory Failure as it does not overload the

lungs or blood saturation of the patients. In contrast,

a liberal approach that is often taken by manual

adjustment of oxygenation, where a high pressure of

oxygen is provided when it is unnecessary, can result

in detrimental effects on the health of the patient and

in some cases even lead to an increase in mortality

rate (Shenoy, 2020).

This study comprised of observing and surveying

different age group of high-risk 20 patients,

particularly ages 40-60, suffering from COVID-19 in

2020. As a result, the percentage of time spent within

the target SpO2 range was observed for the

aforementioned age groups.

The data (Figure 4) indicates that the automated

oxygenation methodology is a better approach than

manual control for a specifically prescribed interval.

In this study, the target range for oxygen saturation

was defined as 90% to 94% SpO2. The graphs show

that for both age groups of 40-50 and 50-60, ALC

controller performed significantly better by

maintaining the saturation level within the target

range 77% and 80.1% of the time. Meanwhile, the

manual methodology was only able to keep the

(a) Patient age group from 40 to 50 years.

(b) Patient age group from 50 to 60 years.

Figure 4: Fraction of time with Oxygen saturation levels of

(a) 40 to 50 and (b) 50 to 60 COVID-19 Patients. It provides

a detailed comparison of the percentage of time spent by

patients in various oxygen saturation ranges when their

oxygenation was controlled manually or via ALC method.

It can be observed that for patients aged 40-50, the

automatic mode opted for a more liberal oxygenation

approach to bring the patient’s SpO2 levels within target.

BIOINFORMATICS 2021 - 12th International Conference on Bioinformatics Models, Methods and Algorithms

188

patients within target saturation 49.5% and 59.6% of

the time for ago groups 40-50 and 50-60 respectively.

Additionally, it can also be seen that for patients

within 40-50 age groups, the manual control by staff

took a more liberal oxygenation approach despite its

potential drawbacks. There can be several reasons

that can range from the severity of the oxygen

required by the patient to the fact that the staff is busy

and overloaded which is why they prefer to set at high

pressures to ensure that the patient does not become

hypoxic in their absence. On the other hand,

automatic controller spent more fraction of time

above target range for 50-60 age groups thus

indicating a more liberal approach as compared to

manual control. Older patients often struggle with

breathing and other respiratory limitations that can be

further exacerbated through COVID-19. In this case,

the learning behavior of the ALC controller is

emphasized as it is using a proactive approach to

maintain high oxygenation to prevent patients from

becoming hypoxic. It is also important to note that the

percent of time spent by patients at the saturation

level of hypoxia (SpO2 < 85%) or approaching

hypoxia (85% < SpO2 ≤ 90%) was significantly

lower for automatic control as compared to manual

control by staff.

Figure 4 also indicates that the patient remains

within the target range of oxygen saturation for a

longer duration when the oxygen is controlled using

the adaptive controller as compared to manual

control. This is beneficial for the patient because now

they are receiving the optimal level of oxygen for a

longer duration resulting in less pressure on their

lungs therefore reducing the load.

Figure 5 is a time response graph. The graph

shows that the automatic controller brings the patient

back to the target oxygen saturation in a shorter

amount of time as compared to the manual control.

This shows that if the oxygen varies, then it is quickly

returned to the required amount resulting in less effect

on the lungs. Less load on the lungs and quicker

response time can lead to faster patient recovery.

Figure 5: Time response of adaptive learning controller and

manual control by staff.

ΔT= T(O2) – T(O1), where T(O2) is the time at

which the oxygen returned to target range and T(O1)

is the time at which oxygen levels dropped/rose from

the target range. Automatic Control should have

lower delta T while manual should have higher

indicating that the automatic control, continuously

adjusts the oxygen levels resulting in faster response.

The step response of adaptive learning controller

tuning shown in Figure 6 show that the system will

reach the stability quickly than the system under the

conventional PID controller and the peak overshoot is

decrease, where the system takes short time to reach

the steady state and that the system got good response

as shown in Figure 6.

Figure 6: Step responses of Adaptive Learning PID

controller Vs simple PID controller.

Table 2: The comparison of different techniques and with

respect to accuracy and complexity of the techniques

applied with other controllers.

References (Zhang,

2015)

(Iobbi,

2006)

(Dong,

2012)

Proposed

Technique Smart

phone

and

browser

server

controlle

d with a

valve

Fuzzy

Contro

l with

PID

Learning

PID

controller

Accuracy High High High High

Complexity High High High Low

5 CONCLUSIONS

The proposed system is designed to provide a

proactive supportive care to COVID-19 patients

instead of reactive care. The key difference between

the two types of care is the fact the former is

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

189

predictive of the variation in oxygen saturation level

of the patient and therefore can make decisions before

or instantaneously to prevent any further detriment of

the patient’s condition. This study focused on the age

groups between 40-50 and 50-60 as they are most

susceptible to chronic respiratory or acute hypoxic

respiratory failure caused by SARS-COV-2. An

adaptive learning controller was used to monitor and

control the oxygenation of these patients and the

response to recovery was recorded and compared

with manual control of oxygenation by healthcare

staff.

It can be seen from Figure 4 that patients’ SpO2

levels were maintained within the target range for

77% and 80.1% whereas for manual control the time

spent by patients within target range was a mere 49.55

and 50.6% for 40-50 year olds and 50-60 year olds

respectively. This is a clear indicator that the

automated control methodology not only maintains

the concentration more consistently, but it also

provides fine adjustments (shown in Figure 5) to

counter any variations that it has experienced in the

past through its predictive algorithm. Figure 6 also

shows that the controller achieves steady state

without a high over-shoot which is beneficial for the

patient as in the case of rapid health deterioration, it

is imperative that the controller be able to meet the

accurate demand of the patient as quickly as possible.

Finally, the PID approach is not only accurate but it

is also easy to implement as compared to other

approaches thus making it cost effective and easy to

implement in case of emergencies as in the case of the

current pandemic.

The results demonstrated that the automatic

control methodology had two major advantages that

are considered key to faster patient recover. The first

advantage is that it was able to prevent patients

becoming hypoxic by quickly adjusting oxygenation

and predicting their oxygen saturation variation based

on their SpO2 variation history. Secondly, the

automatic controller was able to maintain the patients

in the target range for a greater amount of time thus

ensuring that their oxygen concentration levels

remain consistent for greater durations of time. These

two combined benefits can be attributed to faster

recovery of patients as it leads to less stress on their

lungs.

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BIOINFORMATICS 2021 - 12th International Conference on Bioinformatics Models, Methods and Algorithms

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