=Paper=
{{Paper
|id=Vol-3149/short2
|storemode=property
|title=IoT-based Pain Monitoring and Management System (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3149/short2.pdf
|volume=Vol-3149
|authors=Fouad Jameel Ibrahim Alazzawi,Marwa Azzawi,Madiha Fouad Jameel,Yurii Khlaponin
|dblpUrl=https://dblp.org/rec/conf/ttsiit/AlazzawiAJK22
}}
==IoT-based Pain Monitoring and Management System (short paper)==
https://ceur-ws.org/Vol-3149/short2.pdf
IoT-based Pain Monitoring and Management System
Fouad Jameel Ibrahim Alazzawia, Marwa Azzawib, Madiha Fouad Jameelc, and Yurii Khlaponind
a
Computer Engineering Department, Al-Rafidain University college, Baghdad, Iraq
b
Biomedical Engineering Department, Al-Nahrain University , Baghdad, Iraq
c
Department of Dentistry, Al-Rafidain University college, Baghdad, Iraq
d
Kyiv National University of Construction and Architecture, Povitriflotskyi Ave., 31, 03037, Kyiv, Ukraine
Abstract
Patient suffering from pain is in need for an immediate medical intervention, however in some
cases self-pain assessment is not available due to unconsciousness or prone to errors due to
observer’s biases. Therefore, automated pain assessment and management is needed. The
internet of things (IoT) revolution along with biosensor technology could be convenient for pain
assessment and management application. Therefore, this paper is a mini-survey of the
literatures in this field published in six years (2016–2021) was conducted in three online
databases. Hundreds of papers were found, however after title, abstracta and contents
screening only 13 papers were included. This paper is aimed to review the papers that
suggest a pain assessment model in a IoT philosophy, in order to summarize the present
work and propose new suggestions for future work. Research with different pain levels, in
a bigger and real patient population with different diseases were suggested in the conclusion
for future work.
Keywords1
Internet of Things, pain monitoring, pain management, biosensor.
1. Introduction
Pain is the fifth vital sign besides the temperature, pulse rate, respiration rate and blood pressure.
Furthermore, it is one of the most warning signs for seeking medical consideration [1]. Mostly, pain could
significantly contribute the quality of life and cause psychological disturbance including depression,
sleeping disorder, anxiety and fatigue which lead to physiological problems. Therefore, adequate
assessment of pain is essential for precise determining of appropriate treatment [2].
Commonly, pain assessment is done through a self-report or different observational pain scales such as
Numeric rating scale (NRS) [3] and Visual Analogue Scale for Pain (VAS) [4]. However, self-reporting
method could be prone to different types of errors, including the subjective biases of the observer and
patient’s ability to express the amount of perceived pain. Moreover, under special circumstance as in the
patients who are noncommunicative it is not possible to assess the amount of pain. On the other hand,
accurate pain management relies on continuous and precise pain assessment, however, it is impractical to
continuously monitor pain by humans. Furthermore, overdosing of pain-killers could be a life-threatening
problem that may cause a prolonged sedation or hepatic injury [5].
Emerging Technology Trends on the Smart Industry and the Internet of Things, January 19, 2022, Kyiv, Ukraine
EMAIL: Fouad.alazzawi@ruc.edu.iq (F. J. I. Alazzawi); marwa.azzawi@yahoo.com (M. Azzawi); madiha.fouad.elc@ruc.edu.iq (M. F. Jameel),
y.khlaponin@gmail.com (Y. Khlaponin)
ORCID: 0000-0002-2975-3618 (F. J. I. Alazzawi); 0000-0002-3617-6046 (M. Azzawi); 0000-0002-2968-9599 (M. F. Jameel), 0000-0002-9287-
0817 (Y. Khlaponin)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
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Therefore, researchers through the last decade tried to identify the alter of different physiological signals
as a consequence of pain suffering, including electromyography (EMG) [6], electrocardiography (ECG)
[7], electroencephalography (EEG) [8] and photoplethysmography (PPG) [9] in real-time monitoring. ECG
are the process of measuring the small electrical fluctuation produced by the cardiac muscle and it gives as
indication about blood flow, while EEG evaluated brain’s electrical activity and the EMG tracks the
electrical activity of the muscles. Moreover, PPG signal measures blood volume change using irradiation
light applied non-invasively to peripheral body sites. All these signals show alteration and abnormalities
during suffering from pain. Galvanic Skin Response (GSR) is another characteristic that exhibits fluctuation
in the signal during pain due to change of skin conductance.
In the last years, physiological parameters such as heartbeat is not assessed through a clinical and
physical examination. Today, a smartwatch can identify heartbeat and the observed data is sent to a cloud
through a wireless network to be observed by physician, hospital, and other stake holders. Furthermore,
data is stored in a digital form and analyzed for machine deep learning (ML) purposes. All the previous
process is done through what is known as the internet of things (IoT) which connect computer and
healthcare provider together to make our life easier [10]. Figure 1 illustrates the lifecycle of the IoT assisted
wearable sensor system in health care.
Figure 1: Data processing lifecycle IoT assisted wearable sensor systems in healthcare [11]
In this mini-review, the aim is to overview, consolidate and summarize the work that has been done over
the last six years to automatically assess pain from different biomedical signals including EEG, EMG, PPG
and GSR, to identify the challenges and determine the direction of future researches. In this paper, a survey
of “IoT” and “pain” database is done in three different online databases. Then a focused reading, studying
and categorization followed by concluding is performed and many papers were excluded in this stage
according to the methodology described in the second section of this paper. All the results of the survey of
the databases containing IoT using in paint monitoring and controlling were discussed and presented in
section three. Section four concludes the paper and discusses the future opportunities of such work.
2. Methodology
Papers in this mini-review were collected by searching three online database, including google scholar,
ScienceDirect and IEEE Xplore. All the research articles, review articles, book chapters, conference papers
were included in the research, however, the review articles were excluded during results analysis and
discussion. The research was conducted during December 2021 and is limited to six years period, which
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is between 2016 and 2021. Furthermore, the search was limited to English language papers and the search
keywords was ‘IoT’ OR ‘Internet of Things’ AND ‘pain assessment’ OR ‘pain monitoring’.
The inclusion criteria in this mini-review were based on 1) any scientific paper that describe IoT-based
solution for pain management, 2) all included papers should mentioned the word ‘internet of things’ or
‘IoT’ or any technique used in IoT, 3) review articles, mini-review articles, papers with only an abstract
and case reports were excluded.
The papers were collected for review and the following points were focused on during reading:
The paper is about pain detection or assessment not targeted emotions, mental health, happiness or
sadness and stress.
The paper is targeted a patient with pain or a disease that causes pain not affecting the healthy
person with a painful stimulus like heat to cause pain and assess that pain accordingly.
The physiological parameters detected, or the biosensor used, on the other hand the IoT technology
used in the work.
3. Results and Discussion
Three databases were searched for suitable papers depending on the review methodology of this mini-
review. Hundreds of papers were found then they were filtered after reading the abstract. For instance, in
the ScienceDirect database 274 papers were found in the period between 2017–2021, and they are
distributed as per Fig. 1. As per figure the subject started to gain interest more after 2019.
However, after reading the abstract many papers have been excluded due to many criteria, for instance
miss understanding between pain and mental health and emotions which is not an indication of pain
specifically. Moreover, papers that were not about medical application of pain assessment were excluded
too.
The over all papers after all the exclusion were 12 papers as listed according to the year of publication
in Table 1. Physiological parameter or biosensor used, medical problem targeted, IoT-based technology
used and wither the paper worked on pain assessment or pain management or both were mentioned in
Table 1.
Figure 2: Numbers of papers published during the period 2017–2021
in the ScienceDirect database
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As per Table 1 the “pain assessment” and “IoT” subject work started to increase after 2019. For instance,
in 2020 (6/12) papers in pain assessment and IoT technology in medical application were published.
Furthermore, (7/12) of the papers were on patients after surgeries especially orthopedic ones like joint
replacement surgeries and in intensive care unit. Mostly, the reason behind that is poorly controlled post-
operative pain correlated with prolonged duration of recovery, impairment of daily life and eventually
increase morbidity, according to reference [24]. Therefore, continuous assessment and control of pain
through applying of drug analgesia according to pain intensity is a cutting-edge subject that would cost
lower health-care expenses in addition to adequate treatment and recovery. Furthermore, few papers were
about specific disease and pain caused by them like tension-type headache (TTH) and Sickle cell disease.
One paper was about remote rehabilitation of elbow and control of pain due excessive movement, through
a feedback process. Fig. 3 shows an overview of the control architecture.
Table 1: A list of the reviewed references according to year of publication. The physiological
parameter or biosensor, medical problem, IoT-based technology and Pain monitoring and
management are mentioned
Physiological
Year of IoT-based Pain Pain
publication
Parameter or Medical problem
Technology monitoring management Ref.
biosensor
Facial expression Wi-Fi with a cloud
2016
using surface EMG
Patient with pain
serve
Yes No [12]
Mobile web
Facial expression Intensive care
2018
using surface EMG unit patients
application with Yes No [13]
cloud server
PPG depending on A model builds on
Post operative
2019 pulse counting
patient
multiple logistic Yes No [14]
analysis regression
Physiological
Year of IoT-based Pain Pain
publication
Parameter or Medical problem
Technology monitoring management Ref.
biosensor
High-density pain-
Auto encoder model
2020 evoked EEG Patient with pain
based on CNN1
Yes No [15]
potential
2
Sickle cell Four binary ML
2020 Clinical notes
disease patient classifier
Yes No [16]
Peripheral blood
flow and skin ability Several
2020 to conduct Patient with pain communication Yes No [17]
electricity using protocol for IoT
PPG and GSR
Yes
GSR, EMG and
2020
EEG signals
TTH FOG computing Yes (Biofeedback [18]
therapy)
Remote elbow
2020 EMG signal
rehabilitation
Fuzzy logic Yes Yes [19]
Force and sweat Indication of GSM through cloud
2020
sensor labor pain server
Yes No [20]
Electrodermal Post-operative
2021
activity of GSR patient
ML algorithms Yes No [21]
Post-operative
2021 PPG spectrogram pain in conscious CNN Yes No [22]
patient
Post-operative
Heart rate and body bone and joint System based on Yes
2021
temperature replacement NOA3 Yes (Drug analgesia) [23]
surgery
1
CNN is a convolutional neural network; 2 ML is a machine learning; 3 NOA is a neuro optimization algorithm.
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Figure 3: Control architecture overview.
Facial expression using a surface EMG biosensor for pain assessment was one of the technique used to
quantify the pain, however no works were found after 2018. The reasons behind this is claimed to be that
this technique faces several challenges related to data acquisition and development, on the other hand pain
assessment system should be capable to be adapted according to patient facial morphology and texture [25].
However, Yang et al. [13] have designed a wearable mask based on surface EMG signal for continuous
pain assessment as per Fig. 4.
Figure 4: Facial mask for continuous pain assessment design concept and electrode embedded
in addition to the complete wearable device
Furthermore, a paper was about labor pain assessment using a belt around the belly that sense the sweat
using a GSR sensor and the force generated by delivery contractions. Biosensors used in papers were
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equally used in (4/12) for GSR, EMG and PPG, however EEG was the less commonly used due the artifacts
and requirement of high data transmission rate and large storage capacity [17].
4. Conclusions
As a conclusion, pain monitoring and control is a worthy and wide-range research topic. The studies
mentioned in this mini-review are poor in many aspect, for instance, number of applied patients, scale of
pain level and connection with medication management system. However, several biosensors and IoT
technologies have been covered by the researchers, but the effective application of the automated pain
assessment is yet to come.
This mini-review discussed a total of 12 paper in pain assessment ant management using IoT-technology
in many medical applications. Future studies can be more precise in the medical application they targeted,
for instance, considering cancer patients who suffers from sever pains and in need for medication control,
also patients with high rate of heart stroke who suffers from severe chest pain and in need for immediate
medical intervention.
Furthermore, future studies concentration on Fog networking, that can support the big data structure and
large cloud system with large geographical distribution are expected to further improve performance.
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