=Paper=
{{Paper
|id=Vol-3795/icaiw_wseai_3
|storemode=property
|title=IoT Solutions to Assist Patients With Diabetic Foot: A Systematic Mapping
|pdfUrl=https://ceur-ws.org/Vol-3795/icaiw_wseai_3.pdf
|volume=Vol-3795
|authors=Karla Haryanna S. Moura,Valéria Lelli,Fabiana G. Marinho
|dblpUrl=https://dblp.org/rec/conf/icai2/MouraLM24
}}
==IoT Solutions to Assist Patients With Diabetic Foot: A Systematic Mapping==
https://ceur-ws.org/Vol-3795/icaiw_wseai_3.pdf
IoT Solutions to Assist Patients With Diabetic Foot: A
Systematic Mapping
Karla Haryanna S. Moura1 , Valéria Lelli1,* and Fabiana G. Marinho2
1
Federal University of Ceará, Fortaleza, Brazil
2
Federal Institute of Education, Science and Technology of Ceará, Fortaleza, Brazil
Abstract
The use of Internet of Things (IoT) applications in the prevention and treatment of diabetic foot has stimulated
increasing research interest. Researchers are actively developing strategies to create applications that enable
early and personalized interventions for patients with diabetic foot. Additionally, there is a growing focus on
leveraging these technologies to enhance patient monitoring effectiveness. This paper provides a systematic
mapping of research studies addressing the use of IoT in diabetic foot prevention and treatment. The mapping
identified 22 relevant studies proposing various approaches, including smart shoes, smart insoles, and smart socks
for remote monitoring. These studies also explored IoT strategies that integrate, cloud computing, prediction
algorithms and machine learning to store and analyze collected data. This paper discusses the challenges faced
on developing IoT applications for diabetic patients, emphasizing issues related to application architecture, the
scope of solutions, the technologies used, and data integration.
Keywords
Diabetic foot, Internet of things, Internet of medical things, Health care
1. Introduction
The Internet of Things (IoT) is recognized as a fundamental technology to significantly expand the
reach and usefulness of the Internet. By establishing a global infrastructure of interconnected physical
components, IoT integrates a variety of devices, including sensors, actuators, and Radio Frequency
Identification (RFID) systems [1]. Through the collection, analysis, and exchange of data in real time,
this interconnection of devices enables more agile and accurate decision-making, while also facilitating
the creation of highly adaptable and customizable systems. As a consequence, IoT is profoundly
revolutionizing our interaction with the physical world, opening doors to countless opportunities for
innovation, process optimization and development of new business models [2].
The growing interest in the application of IoT in healthcare is especially significant when considering
its impact on remote patient monitoring [3]. Health monitoring technologies have emerged as effective
tools for the prevention, early detection and management of chronic conditions [4].These technologies
present a diversity of wearable devices and have been developed with the purpose of supporting the
independence of the elderly, assisting in post-operative rehabilitation and facilitating the analysis and
improvement of individual skills related to health, techniques, or sports [5].
Applications developed with IoT technologies for diabetic foot play a fundamental role in diagnosing
and monitoring physical condition, integrating sensors into the human body or clothing with the
purpose of monitoring physiological parameters that reflect health status, physical activity and others.
Additionally, these systems are capable of measuring a variety of vital sign parameters in real time and
forwarding them through a network of wireless sensors to family members, specialists, and caregivers
accompanying the patient [6]. This approach not only allows for a rapid and appropriate response
when necessary, but also assists in preventing the progression of chronic and progressive diseases that
require constant monitoring [7].
ICAIW 2024: Workshops at the 7th International Conference on Applied Informatics 2024, October 24–26, 2024, Viña del Mar, Chile
*
Corresponding author.
$ karlaharyanna@alu.ufc.br (K. H. S. Moura); valerialellids@ufc.br (V. Lelli); fabiana.gomes@ifce.edu.br (F. G. Marinho)
0000-0002-0177-3385 (K. H. S. Moura); 0000-0002-1210-7935 (V. Lelli); 0009-0006-8200-4567 (F. G. Marinho)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
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Karla Haryanna S. Moura et al. CEUR Workshop Proceedings 332–346
In this context, considering the relevance of IoT in healthcare and the problem of continuously
monitoring patients with diabetic foot, the main objective of this research is to investigate IoT solutions
that offer support to help patients with this condition. To achieve this goal, this study will conduct a
systematic mapping to investigate the current state of IoT solutions aimed at enhancing the prevention
and effective treatment of diabetic foot. The study will explore the following research questions:
• (RQ1) What are the proposed IoT solutions for patients with diabetic foot?
• (RQ2) What technologies are most used in the development of IoT applications for patients with
diabetic foot?
• (RQ3) What is the scope, in terms of functional requirements, of IoT applications for diabetic
foot care?
• (RQ4) What are the most relevant non-functional requirements of IoT applications for diabetic
foot care?
• (RQ5) What are the challenges faced in developing IoT applications for diabetic foot care?
Based on the research questions mentioned, 22 studies were selected and underwent quality assess-
ment, data extraction and analysis. This selection was carried out in accordance with Kitchenham’s
guidelines [8] and incorporating the backward snowballing technique.
The structure of this article is as follows: Section 2 provides theoretical foundation for understanding
the research; Section 3 analyzes related studies; Section 4 details the methodology used; Section 5
presents the research findings and offers a discussion of results addressing the research questions; and
Section 6 presents the final considerations and future directions.
2. Background
This section presents key concepts related to the application of IoT technology in the monitoring and
management of diabetic foot conditions. It highlights the critical role IoT plays in enhancing the early
diagnosis, treatment, and continuous monitoring of chronic diabetic foot complications.
2.1. Internet of Things - IoT
IoT is characterized as a network of interconnected devices, sensors, and systems that communicate and
share data [9] and represents a scenario in which objects can relay information over a network without
the need for human-to-human or human-to-computer interaction [10]. This infrastructure supports the
creation of collaborative services and applications, catalyzing transformation across sectors such as
education, logistics, healthcare, and smart cities [11].
An IoT infrastructure relies on the integration of sensors and the interoperability of various devices,
thus requiring reliable software design for real-time processing and analysis of large volumes of data.
Rigorous testing and validation are crucial to ensuring software reliability and performance. Additionally,
intuitive user interfaces are essential for enhancing the interaction between users and IoT devices, and
consequently promoting the adoption of these technologies [12].
According to Kassab [13], an architecture of a typical IoT solution includes components such as
sensors, devices, connectivity, data processing and applications. These elements are detailed below.
• Sensors are devices that capture and measure data from either the physical environment or the
human body. This includes a wide range of parameters such as temperature, blood pressure, and
blood glucose levels, among others. By providing accurate and real-time data, sensors play a
crucial role in monitoring health conditions and supporting informed medical decisions.
• Devices encompass a variety of equipment including medical devices, wearable technologies,
and other connected gadgets that utilize data collected by sensors. These devices process, analyze,
and sometimes transmit data to provide insights or alerts regarding health conditions, thereby
facilitating timely medical interventions and ongoing health management.
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• Connectivity refers to the process of transmitting data collected by sensors to cloud-based
or local servers using various communication technologies such as Wi-Fi, Bluetooth, or other
networking methods. This ensures that the data can be accessed and analyzed remotely, enabling
real-time monitoring and effective management of health conditions.
• Data processing involves analyzing data obtained from sensors to extract relevant informa-
tion about the patient’s condition. This analysis may include advanced techniques, such as
machine learning, to identify patterns, predict potential health events, and generate actionable
recommendations for personalized care.
• Applications include platforms and tools designed for visualizing and interpreting processed
data. These applications offer feedback to both patients and healthcare professionals, enhancing
the ability to make informed decisions about health management and treatment plans.
2.2. Diabetic Foot
Diabetic foot refers to a complex condition characterized by infection, ulceration, or tissue damage
in individuals with diabetes, often resulting from complications such as peripheral neuropathy and
peripheral artery disease. Affecting approximately 18.6 million people globally each year, diabetic foot
significantly increases the risk of amputation and mortality [14].
A comprehensive review of the literature identifies four critical parameters for predicting the onset
of diabetic foot: temperature [15], pressure [15], humidity [16], and suboptimal oxygenation levels
(SpO2) [17]. Each of these parameters is briefly discussed below.
2.2.1. Temperature
According to Kulkarni et al. [15], temperature measurements are considered a crucial parameter for
non-invasive remote monitoring of diabetic foot. Research on dermal thermometry has indicated that
temperature variations of 4 °F (greater than 2.2 °C) may serve as valuable indicators for monitoring skin
health and detecting potential issues [18]. However, the lack of a standardized reference range poses a
challenge, as body temperature can vary significantly between individuals and even across different
areas of the same person’s body.
2.2.2. Pressure
There is extensive literature examining the relationship between increased foot pressure and its correla-
tion with ulcer formation in diabetic patients [15]. Pressure measurements can be categorized into static
techniques, which assess pressure while the individual is standing still, and dynamic measurements,
which capture pressure during movement. It is well established that areas of abnormally high pressure
are prevalent in individuals with a history of ulceration, making these pressure points a critical factor
in the recurrence and development of ulcers in diabetic patients [19].
2.2.3. Humidity
Jones et al. [16] highlight that moisture plays a pivotal role in foot health by increasing friction
between the skin and surfaces, such as insoles, potentially leading to tissue deformation when skin
layers move tangentially relative to each other during movement. Both insufficient and excessive
moisture can disrupt the delicate balance necessary for maintaining dermal foot health. Insufficient
sweating, a frequent complication of diabetes, impairs the skin’s barrier function, rendering it more
vulnerable to infections. Furthermore, research has shown a positive correlation between dry skin and
the development of diabetic foot.
2.2.4. Suboptimal Oxygenation
Suboptimal oxygenation (SpO2) is a critical factor that impedes the healing of diabetic wounds by
restricting blood flow to the affected areas, thereby delaying recovery [20]. Monitoring SpO2 levels can
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facilitate the early detection of various complications, including hypoxemia, declining organ function,
tissues at risk of developing wounds, and even cardiac arrest [21].
2.3. IoT in Diabetic Foot
IoT solutions can help monitor and prevent diabetic foot complications by using sensor-equipped
devices to track patients’ physical conditions. Figure 1 depicts an IoT application for patient monitoring
using sensors that measure pressure, temperature, and humidity.
The collected data is transmitted continuously to a mobile app, which employs data analysis tech-
niques to generate alerts for patients, caregivers, and doctors. These sensors can be embedded in
devices like smart shoes, smart insoles, and smart socks. Such applications highlight the importance of
empowering diabetic foot patients with intelligent IoT tools for effective self-management.
Figure 1: Scenario of an IoT solution for monitoring diabetic foot
3. Related Work
This section presents a comprehensive overview of secondary studies investigating the application
of IoT solutions in diabetes management, as well as those focusing specifically on the monitoring of
diabetic foot conditions.
The literature includes review studies that analyze solutions focused on improving healthcare for
diabetic patients through the integration of remote health monitoring (RHM) and Internet of Medical
Things (IoMT) technologies. AlShorman et al. [22] provide a discussion of the current state of digital
health processes, including the utilization of sensors, wearable devices, IoT, and big data tools for
monitoring diabetic patients more effectively.
The systematic review conducted by Souza et al. [3] provides an overview of the current state of the
art regarding data collection methods, system characteristics, and capabilities, and current research
challenges and limitations to be addressed. Similarly, Peyroteo et al. [23] provide an overview of
complementary technologies and address various challenges associated with the development of an
advanced smart healthcare system. This systematic review aims at providing an overview of the state of
the art regarding smart wearable systems (SWS) applications to monitor the status of patients suffering
from vascular disorders of the lower extremity. In alignment with these objectives, Dwivedi et al. [24]
highlight recent advancements in the prevention, diagnosis, and treatment of diabetes.
In the context of diabetic foot, we did not identify any secondary studies focused on IoT solutions.
However, Vaishnavi et al. [25], in their survey, highlight the critical aspects of continuous monitoring
and prediction, which are vital for the effective management of diabetic foot in the context of IoMT.
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Analysis of related works shows that, despite medical advancements, effectively managing diabetic foot
remains a significant challenge.
4. Research Method
The systematic mapping performed in this study adhered to the guidelines set forth by Kitchenham [26],
while also incorporating the backward snowballing technique. Following these established guidelines,
the planning, conduction, and quality assessment of the studies were systematically carried out in
alignment with the activities associated with each phase. It is noteworthy that the Parsifal 1 tool was
used to assist in the entire systematic mapping process.
4.1. Planning
The planning phase entails the development of a comprehensive protocol that clearly defines the
research goals, key research questions, search strategy, inclusion and exclusion criteria, and the specific
databases to be utilized for sourcing relevant literature. The full protocol is available in the study’s
linked repository2 .
4.1.1. Goals and Research Questions
This systematic mapping aimed to identify IoT solutions for preventing and treating diabetic foot,
guided by the primary research question:
• What is the state of the art in IoT-based solutions for preventing and treating diabetic foot?
Research questions (RQs) were formulated, based on the primary research question, and structured
according to the PICOC framework, as outlined below:
• Population (P): IoT solutions;
• Intervention (I): smart health studies to prevent and treat diabetic foot;
• Comparison (C): the comparison dimension was not applicable to the present study;
• Outcome (O): functional and non-functional requirements, architectural characteristics, as
well as associated processes and methodologies; and
• Context (C): smart health.
Table 1 presents de RQs to investigate the challenges, trends, and gaps in IoT solutions for diabetic
foot care.
4.1.2. Search String
The search string was developed using keywords identified from the goal and research questions,
combining logical operators (OR/AND) and customized search strategies for each database. The final
search string used in this study is presented in Table 2.
4.1.3. Selection Criteria
Inclusion and exclusion criteria were defined to select studies that significantly contribute to the research
objectives. Two inclusion criteria and five exclusion criteria were established, as detailed in Table 3.
1
https://parsif.al/
2
https://drive.google.com/drive/folders/161eVvKjN4wJAxeJCVnm5ODrHon3CS35-?usp=sharing
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Table 1
Research questions and their rationale
Research Question
RQ1. What are the proposed IoT solutions for patients with diabetic foot?
Rationale: Identify ways to improve support for the growing need for healthcare IoT applications to enhance
diabetic foot management.
RQ2. What technologies are most used in the development of IoT applications for patients with diabetic foot?
Rationale: Explore emerging technologies and gaps in current diabetic foot monitoring practices.
RQ3. What is the scope, in terms of functional requirements, of IoT applications for diabetic foot care?
Rationale: Identify key functionalities for IoT apps to support patients effectively.
RQ4. What are the most relevant non-functional requirements of IoT applications for diabetic foot care?
Rationale: Identify non-functional requirements of IoT applications that impact user, such as satisfaction
and safety.
RQ5. What are the challenges faced in developing IoT applications for diabetic foot care?
Rationale: Explore trends and challenges for new research in Software Engineering.
Table 2
Search String
(“IoT” OR “Internet of things” OR IoMT OR “internet of medical things” OR “e-
health” OR ehealth OR mhealth OR IoHT OR “internet of healthcare things” OR
“m-health” OR “mobile health” OR “smart healthcare” OR “smart health care”) AND
(“diabetic foot” OR “diabetes pathology” OR “diabetic pathology” OR “foot disease”
OR “pathology of diabetes” OR “pathology of diabetes”)
Table 3
Inclusion and Exclusion Criteria
ID Description
Inclusion Criteria (IC)
IC1 Studies that present an IoT solution to treat or prevent diabetic foot
IC2 Studies that were written in English or Portuguese
Exclusion Criteria (EC)
EC1 Studies that do not explicitly address IoT solutions
EC2 Studies that are not aimed at the diabetic foot
EC3 Studies that are abstracts or conference indexes
EC4 Studies in which the full text is not available
EC5 Studies that are shortened versions of other studies
4.1.4. Databases
To maximize the scope of the mapping, four databases (ACM Digital Library3 , IEEE4 , Scopus5 and
PubMed6 ) were selected based on their extensive, high-quality literature and credibility. Scopus was
chosen for its conference coverage, and PubMed for its broad biomedical literature. The search string
was applied with no restrictions on the publication period.
4.2. Conduction
During the conduction stage, the search string was applied across four selected databases, yielding
226 studies: 107 from Scopus, 65 from PubMed, 32 from IEEE, and 22 from ACM Digital Library. 70
3
http://portal.acm.org
4
https://ieeexplore.ieee.org/
5
http://www.scopus.com
6
https://www.ncbi.nlm.nih.gov
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duplicates, mainly from PubMed and IEEE, were removed.
The remaining 156 studies were reviewed by title and abstract, leading to the exclusion of 96 studies
that were not relevant to the theme. The remaining 60 studies were then fully read to verify their
alignment with the established criteria. From these, 13 studies were selected. The stages of study
selection are illustrated in Figure 2, depicting the number of studies at each step. Table 4 presents these
studies, listing their respective title, year, and reference.
Figure 2: Adapted PRISMA flow diagram from [27]
Backward snowballing was applied to the 13 studies from the initial phase to identify additional
relevant research. Of 476 references reviewed, 27 were duplicates from the first phase. After applying
the selection criteria, 431 studies were excluded based on titles and abstracts. The remaining 18 studies
were fully reviewed, resulting in 9 additional studies.
5. Results and Discussions
This section presents a review of 22 studies identified through systematic mapping and backward
snowballing, intended to address the research questions.
The graph depicted in the Figure 3, illustrates the number of studies identified in the systematic
mapping of IoT solutions to assist patients with diabetic foot. The publication dates of the selected
studies range from 2011 to 2024, showing notable peaks in certain years.
From 2011 to 2018, the numbers remain consistent at 1, followed by a continuous increase starting in
2019, with 2 studies published that year and 3 in 2020. The numbers peak at 4 in 2021, then fluctuate,
dropping to 3 in 2022, rising again to 4 in 2023, and falling to 3 in 2024. Overall, the trend shows growth
beginning in 2019, with significant peaks in 2021 and 2023, and minor fluctuations afterward. The
peak in 2021 indicates a period of notable advancements in the field, likely driven by technological
innovations and a growing recognition of the importance of diabetic foot monitoring.
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Table 4
Selected Studies in Systematic Mapping
Title Year Reference
Planipes: Mobile Foot Pressure Analysis 2011 [28]
IoT Based Monitoring of Foot Pressure Using FSR Sensor 2017 [29]
Health Care Foot Wear for Monitoring the Diabetic Patients 2018 [30]
Dia-Shoe: A Smart Diabetic Shoe to Monitor and Prevent Diabetic Foot Ulcers 2019 [31]
Innovative Intelligent Insole System Reduces Diabetic Foot Ulcer Recurrence at 2019 [32]
Plantar Sites: a Prospective, Randomised, Proof-Of-Concept Study
A Medical IoT-Based Remote Monitoring System: Application on Diabetic Foot 2020 [7]
IoT and Cloud Based Healthcare Solution for Diabetic Foot Ulcer 2020 [33]
An Embedded Wearable Device for Monitoring Diabetic Foot Ulcer Parameters 2020 [15]
Design of Smart Device for Foot of Diabetic Patient in Malaysia 2021 [34]
SISTINE: Sensorized Socks for Telemonitoring of Vascular Disease Patients 2021 [35]
Mobile Health–Based Thermometer for Monitoring Wound Healing After Endovas- 2021 [36]
cular Therapy in Patients With Chronic Foot Ulcer: Prospective Cohort Study
A Smart Wearable Device for Monitoring and Self-Management of Diabetic Foot: A 2021 [37]
Proof of Concept Study
Smart Sock-Based Machine Learning Models Development for Phlebopathic Patient 2022 [38]
Screening
Early Detection of Diabetic Foot Ulcers through Wearable Shoe Design 2022 [39]
A Smart Wearable Oximeter Insole for Monitoring SpO2 Levels of Diabetics Foot 2022 [17]
Ulcer
Development of a Flexible Smart Wearable Oximeter Insole for Monitoring SpO2 2023 [20]
Levels of Diabetics’ Foot Ulcer
Smart Offloading Boot System for Remote Patient Monitoring: Toward Adherence 2023 [40]
Reinforcement and Proper Physical Activity Prescription for Diabetic Foot Ulcer
Patients
Implementation of LoRa Wireless Communication in Smart Diabetic Shoes Design 2023 [41]
Prototype Design of Smart Diabetic Shoes with Lora Module Communication 2023 [42]
Home-based Detection and Prediction of Diabetic Foot Ulcers at Early Stage Using 2024 [43]
Sensor Technology and Supervised Learning
Design and Manufacturing a Smart Shoe for Diabetic Foot Ulcer Monitoring and 2024 [44]
Prediction System Using Internet-Of-Things Technology
Intelligent Pressure and Temperature Sensor Algorithm for Diabetic Patient Monitor- 2024 [45]
ing: An IoT Approach
Figure 3: Articles per year
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5.1. (RQ1) What are the proposed IoT solutions for patients with diabetic foot?
The most promising IoT solutions for patients with diabetic foot include wearable devices such as insoles,
shoes, socks, and boots. These devices are designed to monitor key metrics like vibration, temperature,
pressure, humidity, and SpO2, with temperature being the most commonly used parameter.
Table 5 summarizes the components and methodologies used in monitoring diabetic foot health.
The analysis highlights the variety of sensor types employed, each requiring distinct approaches for
effective monitoring of diabetic foot conditions. For data communication, Bluetooth and Wi-Fi are the
most commonly used protocols. However, some solutions also incorporate advanced technologies such
as ANT+, Wireless LoRa (integrated with Bluetooth or Wi-Fi), 4G, or 5G to enhance data transmission
capabilities.
Table 5
IoT Solutions
Wearable Hardware Communication Software Study
Smart Insole Vibration Sensor Bluetooth Mobile [43]
Pressure | Temperature Sensors Bluetooth Blynk App [45]
Pressure Sensor ANT+ Smartwatch [32]
Force Sensitive Register Bluetooth Mobile [29]
SpO2 Sensor Bluetooth Mobile [20]
Pressure | Temperature Sensors Bluetooth Blynk App [34]
Temperature | Humidity | Pressure Sensors Wi-Fi Mobile [30]
Flexiforce | Pressure Sensors Bluetooth Mobile [33]
Force Sensitive Resistor Bluetooth Mobile [28]
SpO2 Sensor Bluetooth Mobile [17]
Smart Shoes Humidity | Temperature Sensors | Force Wi-Fi Mobile [44]
Sensitive Resistor
Pressure | Temperature Sensors Wireless Lora (in- Web [42]
tegrated with Blue-
tooth or Wi-Fi)
Humidity | Temperature Sensors | Force Bluetooth Mobile [37]
Sensitive Resistor
Temperature | Vibration | Pressure Sensors Wi-Fi Mobile [39]
Temperature Sensor | Force Sensitive Re- Wireless Lora (in- Web [41]
sistor tegrated with Blue-
tooth or Wi-Fi)
Plantar Pressure Scanner | SpO2 | Temper- Bluetooth Mobile [7]
ature Sensors
Temperature | Humidity Sensors | Tilt Wi-Fi Mobile [31]
Switch Sensor | Calibrated Load Cell Sen-
sor
Smart Sock Force Sensitive Resister | Temperature Sen- Wi-Fi Bluetooth Mobile [15]
sor | Accelerometer | Gyroscope | Heart-
Rate Sensor
Pressure | Inertial Sensors Bluetooth | 4G Mobile [38]
Flexiforce | Inertial | Stretch Sensors Bluetooth | 5G Mobile [35]
Smart Boot Accelerometer and Gyroscope Sensor Bluetooth and 4G Smartwatch [40]
Body Temper- Temperature sensor Bluetooth Mobile [36]
ature
Additionally, Table 5 outlines the software platforms associated with these studies, which include
both Mobile, Smartwatch, Blynk and Web-Based applications. Each platform is designed to support
specific aspects of remote patient monitoring and management, facilitating real-time data access and
interaction. These platforms contribute to more effective patient care and informed decision-making.
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5.2. (RQ2) What technologies are most used in the development of IoT applications
for patients with diabetic foot?
The 22 studies analyzed concentrate on the architecture and prototype development of systems involving
sensors, electronic boards, and microcontrollers. They emphasize the design and construction of
IoT frameworks and prototypes that integrate multiple components. Additionally, the examined IoT
solutions incorporate a variety of technologies, as summarized in Table 6.
Table 6
Technologies
Technology Study
LoRa [42]
LoRaWAN [41]
Bluetooth Low Energy [40] [20] [17] [39] [38] [35] [37] [36] [34] [15] [33] [7] [32] [31] [30] [29] [28]
(BLE)
Energy Management [41] [40] [39] [38] [35] [36] [34] [15] [33] [7] [32] [31] [30] [29] [28]
System (EMS)
Blynk Application [44] [45]
Machine Learning | Data [43] [38] [38] [40] [35] [37] [36] [34] [15] [33] [7] [32] [31] [30] [29] [28]
Analysis | Prediction
Web Platform | Smart- [43] [42] [40] [20] [17] [39] [38] [35] [35] [36] [34] [15] [33] [32] [31] [30] [29]
phone Application [28]
Dash Board Web [40] [36] [33] [7]
Cloud Computing [33] [7] [29]
LoRa and LoRaWAN enable long-range, low-power wireless communication for monitoring data
transmission in remote or home-based settings. In addition, the system may also integrate other com-
munication technologies, such as Bluetooth or Wi-Fi, for short-range data transfer and synchronization
with smartphones.
Bluetooth Low Energy (BLE) offers low power consumption and efficient transmission of sensor data
to mobile devices or remote monitoring platforms. Additionally, Energy Management System (EMS) is
designed to optimize battery life and ensure the continuous operation of wearable devices by effectively
managing the power supplied to sensors and microcontrollers.
The Blynk Application provides an interface for real-time data visualization and monitoring on
smartphones. Additionally, Machine Learning, Data Analysis, and Prediction services are applied in
various studies to interpret sensor data, detect patterns, and provide insights into diabetic foot health,
enhancing monitoring accuracy.
The Web Platform and Smartphone Application are used to visualize sensor data and alert patients
or healthcare providers in case of abnormal readings, enabling continuous real-time monitoring and
early prediction of diabetic foot. The Web Dashboard further analyzes sensor data and may incorporate
algorithms to provide feedback on disease progression. Cloud computing processes, stores, and analyzes
data from wearable devices, emphasizing the interpretation of health metrics to identify risks associated
with diabetic foot conditions.
5.3. (RQ3) What is the scope, in terms of functional requirements, of IoT
applications for diabetic foot care?
The functional requirements summarized in Table 7 reveal that Physical Parameters Monitoring, Real-
Time Communication, and Notifications and Alerts are present across all studies. This consistency
indicates that these features are fundamental to all implementations and are crucial for maintaining
continuous monitoring of diabetic foot health and facilitating interventions.
Visualization Interface is featured in 15 of the 22 studies, highlighting the importance of providing
an intuitive platform for patients and healthcare providers to monitor and interpret data in real time. It
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Table 7
Functional Requirements
Functional Requirements Studies
Physical Parameters Monitoring Present in all studies
Real-Time Communication Present in all studies
Notifications and Alerts Present in all studies
Visualization Interface [31] [7] [41] [28] [35] [17] [39] [20] [40] [37] [44] [33] [34] [32] [29]
Data Collection and Storage [31] [7] [41] [35] [28] [17] [39] [20] [40] [37] [36] [43] [38] [15] [32]
[30] [29]
Sensor Calibration [41]
Physical Activity Prescription [40]
Report Generation [33]
Historical Data Recording [34]
emphasizes the role of user-friendly interfaces in ensuring the accessibility of health data.
Data Collection and Storage is another frequently employed requirement, present in 17 studies. This
functionality ensures that data gathered from sensors is stored for future analysis, supporting both
real-time monitoring and long-term tracking of patient health trends.
Sensor Calibration is featured in a single study. This suggests that, despite the importance of sensor
accuracy, it is not always emphasized. Physical Activity Prescription and Report Generation indicate
specific functionalities that are supplementary to particular use cases, such as providing guidance on
patient activity or generating health reports for further analysis.
Although the Historical Data Recording functionality is crucial for tracking the progression of a
patient’s condition over time, adding depth to long-term monitoring and aiding in the identification of
trends or recurrent issues, it is rarely considered in the studies.
5.4. (RQ4) What are the most relevant non-functional requirements of IoT
applications for diabetic foot care?
The non-functional requirements outlined in Table 8 were reviewed to identify the essential character-
istics needed for effective IoT systems.
Table 8
Non Functional Requirements
Non Functional Requirements Studies
Low Cost [31] [36] [43] [15] [42]
Low Power Consumption [31] [7] [41] [42] [35] [32] [30] [29]
Accuracy [31] [35] [28] [17] [39] [20] [40] [37] [36] [43] [38] [15] [42] [45] [32]
[29]
Usability | Comfort [31] [41] [35] [28] [17] [39] [37] [20] [40] [36] [43] [38] [15] [42] [44]
[33] [34] [45] [32] [30] [29]
Device Durability [31] [35] [17] [39] [20] [40] [37] [15] [36] [38] [42] [44] [32] [30]
Data Security | Privacy [7] [28] [35] [17] [39] [20] [40] [37] [36] [43] [38] [15] [42] [44] [33]
[34] [45] [32] [30] [29]
Performance [28] [35] [17] [39] [20] [40] [37] [43] [38] [15] [42] [44] [33] [34] [45]
Low Latency [7] [36]
Reliability [31] [7] [41] [35] [37] [33] [34] [32] [29]
Signal Robustness [41] [41] [43] [38] [45]
Low Cost emphasizes economic viability in system design, making solutions accessible to a wider
audience, while Low Power Consumption highlights the importance of energy efficiency in IoT-based
healthcare devices. Accuracy, emphasized in 16 studies, stresses the critical importance of precision in
monitoring health parameters such as pressure and temperature for diabetic patients.
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Usability and Comfort are cited in nearly all studies and demonstrate the importance of user-friendly
designs in wearable devices to enhance patient adherence. Device Durability is essential for long-term
use, especially in wearable devices that are exposed to various conditions.
Data Security and Privacy, noted in 20 of the 22 studies, are prioritized to protect patient data and
safeguard sensitive medical information against unauthorized access and breaches, ensuring patient
privacy and data integrity.
Performance, Low Latency, Reliability, and Signal Robustness underscore the importance of the
system’s efficiency in executing its functions without delays or failures, ensuring consistent data
transmission without interruption, and accentuating the need for robust systems that can reliably
handle tasks critical for real-time monitoring.
5.5. (RQ5) What are the challenges faced in developing IoT applications for diabetic
foot care?
The studies analyzed in this systematic mapping reveal a range of significant challenges and limitations.
A key issue is the high cost associated with IoT technologies, which often rely on specialized sensors
with considerable price variability. This cost factor can increase the overall price of devices, making
them impractical for large-scale production and inaccessible to a substantial portion of the population.
Several studies mention the limitation of small sample sizes, which significantly undermines the
statistical power and reliability of their findings. Furthermore, the studies predominantly relied on
data gathered during proof-of-concept stages or short-term trials, restricting the generalizability and
applicability of their conclusions.
Another critical challenge is ensuring the usability of IoT devices, which is vital for promoting patient
adherence to treatment. Poorly designed user interfaces can significantly hinder user engagement and
compromise treatment outcomes. Despite studies mentioning its importance, they report substantial
obstacles in achieving participant adherence to sensor usage, citing discomfort with wearing the devices,
an increased risk of falls or accidents due to non-standard designs compared to traditional footwear,
and mobility limitations when sensors are worn on only one foot.
For IoT solutions to be effective, they must perform reliably across all functions, including data
reception, transmission, feedback, and processing. Accuracy is paramount in medical systems, but
technical shortcomings such as delayed response times, limited battery life, communication failures
between sensors, and inaccuracies in sensor measurements pose significant barriers to effectiveness.
These inaccuracies are compounded by factors such as asymmetry resulting from using sensors on only
one foot and measurement errors caused by fluctuating environmental conditions like temperature and
humidity. Such limitations severely compromise the reliability of the results.
6. Conclusion
This study employed systematic mapping, following Kitchenham’s guidelines and incorporating the
backward snowballing technique, to investigate the integration IoT technology in managing diabetic
foot disease. The research revealed that monitoring key parameters such as temperature, pressure,
humidity, vibration, and SpO2 bridges the gap between health and technology. This integration supports
early detection of potential issues, enhances treatment efficacy, prevents complications, and ultimately
improves the overall quality of life for patients.
From the reviewed studies, several technical limitations were identified, including challenges related
to prototype planning, sensor placement, and the structural design of wearable devices. Usability
constraints, security concerns, and issues with the effectiveness of data transactions within the network
were also frequently observed, particularly concerning the duration and conditions of device use by
patients. Based on these findings, a prototype of an IoT system for monitoring and detecting risk
symptoms in diabetic foot will be developed. This prototype will address the identified solutions,
functional and non-functional requirements, technologies, and challenges highlighted in the mapping.
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To fully realize the potential of IoT integration in managing diabetic foot disease, future research
must address not only the existing technical limitations but also consider additional factors such as
patient acceptance, healthcare professional training, and cost implications. Evaluating these social and
technical aspects is essential for optimizing the deployment and efficacy of IoT technologies in practical,
real-world settings.
Acknowledgments
The present work was supported by the Federal University of Ceara (UFC), Brazil. We thank CAPES for
the assistance with grant support that helped the first author.
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