=Paper=
{{Paper
|id=Vol-3892/short5
|storemode=property
|title=Internet of Robotic Things (IoRT) Approach to Lifelong Learning and Medical Education With Internet of Medical Things (IoMT)
|pdfUrl=https://ceur-ws.org/Vol-3892/short5.pdf
|volume=Vol-3892
|authors=Roman Hasko,Oleksandra Hasko,Hakan Kutucu
|dblpUrl=https://dblp.org/rec/conf/iddm/HaskoHK24
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==Internet of Robotic Things (IoRT) Approach to Lifelong Learning and Medical Education With Internet of Medical Things (IoMT)==
https://ceur-ws.org/Vol-3892/short5.pdf
Internet of Robotic Things (IoRT) approach to lifelong
learning and medical education with Internet of Medical
Things (IoMT) ⋆
Roman Hasko1,*,†, Oleksandra Hasko1,†, Kutucu Hakan2,†
1
Lviv Polytechnic National University, 12, Stepan Bandera str., Lviv, 79000, Ukraine
2
Karabuk University, Demir Celik Campus, Karabuk, 78050, Turkey
Abstract
The article describes the use of relatively new Internet of Robotic Things (IoRT) and Internet of
Medical Things (IoMT) paradigms as a logical development of the Internet of Things (IoT) concept in
terms of Lifelong Learning and the specifics of medical education from the point of view of the
applied use of robotics and artificial intelligence. The Internet of Robotic Things (IoRT) is specifically
proposed for robotics and will be important for the development of multi-purpose robotic systems.
As the Internet of Things (IoT) provides a reliable framework for connecting things to the Internet
and simplifies machine-to-machine communication and data transfer over core network protocols,
and is developing at such a rapid pace that billions of devices are now connected, with the prospect
of trillions in the coming years, it is understandable to use and the expansion of IoT concepts and
technologies to other fields, in particular robotics in its various applications, such as in the military,
agriculture, industry, health care, and biotechnology. One of these directions is education, especially
lifelong and medical. Another branch of IoT in the medical direction should be highlighted separately,
i.e. Medical Education with Internet of Medical Things (IoMT). IoRT is a symbiosis of various
technologies such as cloud computing, artificial intelligence (AI), machine learning and the Internet
of Things. An example of the implementation of IoRT for education is considered on the basis of the
active university Laboratory of Robotics with collaborative robots (cobots) Dobot MG400 and the
integration of several such cobots into a single Internet-based system based on ROS and applied
applications, which allows teaching new skills and knowledge for the implementation of robotic
systems on based on IoT in real-world implementations.
Keywords
Internet Of Robotic Things, Internet Of Things, Internet of Medical Things, ROS.
1. Introduction
The integration of the Internet of Robotic Things (IoRT) [1, 2] into the educational process
combines robotics and Internet of Things (IoT) technologies [3, 4] to improve the experience and
quality of learning in various disciplines, including lifelong education, and is an innovative
approach. Similarly, the integration of the Internet of Medical Things (IoMT) into medical education
[5] will facilitate better hands-on learning and the development of important skills such as
collaboration, problem-solving and computational thinking, making it a key component in
preparing students for a technological future. The integration of individual robotic parts together
with artificial intelligence and cloud services (IoRT) has attracted attention for its potential to
transform traditional pedagogical practices. Creating an engaging, adaptive learning environment
allows educators to tailor instruction to individual student needs and foster deeper engagement and
motivation. Moreover, the partnership between educational institutions and industry and medicine
is a strategic direction and will allow us to improve educational programs and ensure their
alignment with real tasks and requirements for future education. It is clear that issues such as
accessibility, data privacy and the need to retrain teachers remain some obstacles to the effective
implementation of innovations in education. However, the implementation of the retraining of
IDDM’24: 7th International Conference on Informatics & Data-Driven Medicine, November 14 - 16, 2024, Birmingham, UK
∗
Corresponding author.
†
These authors contributed equally.
roman.t.hasko@lpnu.ua (A. 1); oleksandra.l.hasko@lpnu.ua (A. 2); hakankutucu@karabuk.edu.tr (A. 3)
0000-0001-5923-6577 (A. 1); 0000-0003-4519-610X (A. 2); 0000-0001-7144-7246 (A. 3)
© 2023 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
future teachers on the basis of IoRT/IoMT will make this process really effective. The potential of
IoRT to bridge the gap between theory and practice is a compelling argument for its further
development and integration into modern educational frameworks, although the implementation of
IoRT in education raises some debates about the long-term implications, including ethical issues
related to data security and equitable access to technology. We emphasize and focus more on the
possibilities of creating educational programs that integrate robotics and embrace interdisciplinary
approaches in a holistic educational process.
2. Background
The integration of robotics into education has a rich history that began in the mid-20th century.
Early educational works such as the robot turtle were designed to introduce students to
programming concepts, marking the first steps towards a more interactive and engaging learning
environment. With the development of technology, significant advances in robotics, artificial
intelligence (AI), and educational pedagogy have led to the evolution of robotics in educational
institutions, making it a dynamic and interdisciplinary field.
The modern development of accessible robotics kits, including such popular platforms as LEGO
Mindstorms and Arduino, Raspberry Pi, and Nvidia Jetson, has greatly democratized access to
robotics education on the one hand, and introduced such robotic technologies into a wide range of
interdisciplinary education. This has allowed educators and students worldwide to work with
robotics in ways previously limited to specialized institutions. The affordability of these tools
contributes to the wide implementation of robotics in classrooms and specialized laboratories such
as the Robotics Laboratory at the Lviv National Polytechnic University. It also enriches the learning
experience at various levels of education. In addition, the emergence of educational robotics as a
separate field has created a need for new curricula and teaching methods.
As robotics becomes more popular in educational environments, innovative approaches to
learning that emphasize problem-solving, critical thinking, and creativity are emerging. This shift is
closely related to constructivist educational theories, particularly those proposed by Jean Piaget and
Seymour Papert, who advocate hands-on learning and the importance of computational thinking [6].
The continued growth of robotics in education reflects a broader trend to prepare students for a
technologically advanced workforce, where skills such as analytical thinking, programming, and
teamwork are increasingly valued.
3. Benefits of Integration IoRT/IoMT into Education
Let's take a closer look at the benefits of integrating IoRT/IoMT into education.
- Expanding collaboration skills through the integration of robotics and Internet of Things (IoT)
technologies into education. Students participate in collaborative projects and learn to respect each
other's contributions while advancing their collective knowledge. This process fosters an
environment where transferable skills related to collaborative processes are developed and can be
applied in a variety of contexts outside the classroom.
- An immersive learning experience facilitated by robotics and the Internet of Things that is both
interactive and immersive. Such technologies make it possible to create adaptive learning
environments for the individual needs of students, improving their learning experience. By
integrating advanced technologies such as artificial intelligence (AI) and machine learning (ML),
educators can even create personalized learning paths.
- Lifelong learning and continuous development are the best illustrations of the use of robotics in
education, which not only equips students with current knowledge, but also instills a lifelong
learning mindset. As students work together to overcome challenges and develop solutions, they
develop the critical thinking and problem-solving skills necessary for future success. Also, the
integration of intelligent technologies requires continuous professional development of teachers,
ensuring that they remain adept at creating and using advanced learning environments.
- Combining theory and practice through the integration of robotics into the curriculum allows
students to combine theoretical concepts with practical applications. This experiential learning
fosters a deeper understanding of complex subjects such as science, technology, engineering and
mathematics (STEM). In addition, hands-on experience with robotics, creating authentic learning
opportunities, improves operational skills and emotional development of students.
- Strategic partnerships between educational institutions and industry, medicine and other fields
in the integration of robotics and the Internet of Things in education encourage strategic
partnerships and provide access to advanced technologies and resources that enhance the learning
experience. Such cooperation ensures that educational programs meet the needs of the modern
stage of the development of civilization and prepares students for real challenges.
- Overcoming educational barriers, such as potential difficulties in implementation, lack of
resources or teacher training, the integration of robotics can help overcome these obstacles.
Overcoming them will lead to more effective use of technologies that contribute to a common
learning environment and enriching education.
4. Using of the Internet of Robotic Things and Robotics
Consider the technologies involved. The integration of IoRT into the educational environment
involves several advanced technologies that improve the learning experience and offer innovative
learning solutions:
- The Internet of Things (IoT) plays a crucial role in modern education, facilitating access to
learning materials through any device connected to the Internet, allowing the collection and
analysis of data from students using sensors and wearable technologies, allowing for real-time
monitoring of academic performance time, RFID tags and facial recognition improve control. IoT
applications can optimize the learning environment, such as identifying available learning spaces or
providing access to additional classrooms as needed.
- Educational robotics combines mechanical manufacturing, electronic sensors and artificial
intelligence to create interactive learning tools that directly engage students, enriching their
learning experience and promoting active participation in various disciplines. Using robots in
education promotes critical thinking and computational skills, allowing students to explore
programming and engineering concepts through hands-on experiences.
Figure 1: Example of cooperation between a team of robots and people.
- Artificial intelligence and machine learning provide a personalized learning experience by
analyzing individual learning styles and preferences. These technologies support the development
of intelligent learning systems that adapt to the unique needs of each student, providing targeted
feedback and improving the overall learning process, and help educators understand student
progress and adapt lessons accordingly for success.
- Collaboration technologies such as cobots (collaborative robots) are being integrated into
educational environments, particularly learning environments related to Industry 5.0. These robots
facilitate effective human-robot interaction, increasing teamwork skills and preparing students for
future work environments where they will collaborate with machines [7-9].
- Collaborative learning through robotics involving cross-curricular programs such as English
Language Arts (ELA) or Social Sciences. In our case, the emphasis is on lifelong education for
different categories of students and another separate direction in medical education [10].
Consider the Internet of Things paradigm specifically proposed for robotics, namely the Internet
of Robotic Things (IoRT). IoRT is a collection of various developments such as cloud computing,
artificial intelligence (AI), machine learning and (IoT). An example of an architecture that is
important for the development of multi-purpose robotic systems for IoT is shown in Figure 2.
Figure 2: The example of architecture for the IoRT framework.
4.1 IoRT and ROS2
IoRT (Internet of Robotic Things) and ROS2 (Robot Operating System 2) are two essential concepts
in modern robotics that interact with each other and provide new opportunities for the
development of autonomous systems. IoRT (Internet of Robotic Things) brings together robotics
and the Internet of Things (IoT), creating a network of interconnected robots and devices that can
collect, process, and share data in real-time. The idea is that robots can function in an integrated
ecosystem where they interact not only with each other, but also with various sensors, devices and
infrastructure. The main elements of IoT are сonnectivity, data analytics and autonomy. Robots can
function independently or in a team, adapting to changes in the environment thanks to shared
access to information.
ROS2 (Robot Operating System 2) is the current version of the Robot Operating System, which
is an open platform for developing software for robots. ROS2 is designed to improve real-time
support, security, and cross-platform performance. The main characteristics of ROS2 are modularity,
messaging systems (DDS — Data Distribution Service) and support of different operating systems.
The interaction of IoRT and ROS2 and their synergy creates opportunities for innovation in robotics,
real-time communication allows robots in IoRT to exchange data instantly, implementing
interactive and adaptive workflows. Robots that work as part of IoRT and use ROS2 can easily
integrate with other IoT devices, gaining access to additional information and resources. Using
ROS2, robots can implement complex cooperative scenarios where multiple robots work together to
achieve a common outcome and enabling the creation of more intelligent, adaptive and autonomous
solutions.
When considering IoRT, it is necessary to point out the importance of choosing software for
programming the actual robotic component and its individual parts. The most likely choice would
be the Robot Operating System 2 (ROS 2), which serves as a comprehensive framework designed to
facilitate the development and deployment of robotic applications. It builds on the foundation laid
by the previous version of ROS 1 and, at the same time, addresses the shortcomings that arose when
robotics began to be used more commercially. One of the critical features of ROS 2 is the ability to
decompose complex software systems into manageable components known as nodes. Each node is
responsible for a specific task in the robotic system, which promotes modularity and reusability.
Unlike its predecessor, ROS 1, ROS 2 addresses the complexity and scalability challenges associated
with rapid advances in robotics, making it essential for both academic and industrial environments.
Central to IoRT/IoMT learning is the use of robotics learning platforms that combine simulators,
hands-on activities, and theoretical lessons to create a comprehensive learning environment. Tools
like Turtlesim and other simulation environments allow students to explore fundamental concepts
without the risk of physical hardware. These platforms often emphasize project-based learning,
promoting engagement and practical problem-solving skills, allowing students to apply theoretical
knowledge to real-world problems.
4.2 Robotics and IoT in Medical Education - Internet of Medical
Things
The integration of robotics, particularly the Robot Operating System (ROS) and the use of the
Internet of Things in medical education represents a transformative approach that improves the
medical training and development of future healthcare professionals. This innovative educational
paradigm uses advanced technologies such as simulation and virtual reality to provide immersive
learning experiences that enhance medical competencies and clinical skills. As demand for skilled
healthcare professionals grows, the importance of incorporating IoMT-based platforms into
curricula is increasingly recognized, signaling a shift to modernized learning methodologies that
meet the changing healthcare technology landscape.
Robotic surgery has been a major advance in medicine, offering benefits such as improved
visualization, reduced postoperative complications, and shorter hospital stays. Despite these
advantages, the application of robotics in medical education is slow and often limited to specialized
centers. The use of ROS-based learning platforms improves surgical training by creating a safe,
controlled environment where students can practice complex procedures without risking patient
safety. In addition, project-based learning encourages hands-on experience, fosters a deeper
understanding of robotics concepts, and prepares students for real-world applications in healthcare
settings. Integrating IoMT into medical education is not without its challenges of technological
barriers, resource limitations, and a steep learning curve that can hinder effective implementation.
The Internet of Medical Things (IoMT) is one of the booming fields of the modern era that
focuses on the digitization of healthcare services by connecting hospitals, healthcare resources,
healthcare professionals and patients via the Internet. IoMT currently offers various services such
as patient data management, disease diagnosis, remote health monitoring, telesurgery, etc. The
analysis shows that IoMT is one of the fastest growing areas of information technology (IT), which
uses various sensors, equipment and devices to determine data related to human health and share
data with hospitals, doctors and health professionals for remote diagnosis and treatment [11].
4.3 Cooperation of Robots and Collaborative Robots
The Robotics Lab, with collaborative robots and ROS2, is an efficient environment for learning and
researching robotics. Focus on research and development of technologies of collaborative robots
(cobots) capable of working both in swarms and autonomously, and their use in educational
processes. The main goal is to create systems that can interact and cooperate to solve complex tasks
of learning, manipulation and autonomous navigation. Main focus on machine learning research,
focusing on reinforcement learning techniques where robots can adapt their behavior based on
successes and failures in tasks. Such learning includes cooperative learning (robots work together to
achieve a common goal) and mutual learning (one robot can teach others by sharing acquired
knowledge or strategies).
The practical implementation of the described concepts is carried out in the Laboratory of
Robotics of the Lviv National Polytechnic University. Thanks to the use of many DG400 robots,
ROS2 and the network, their cooperation is possible. Examples of work with DG400 cobots are
shown in Figure 3.
Figure 3: Example of part of IoRT - Cobot Dobot DG400 in actions.
The structure of the laboratory includes
- Robots: The lab is equipped with different types of collaborative robots, such as manipulators,
mobile platforms and drones, which can work individually or as part of a swarm.
- Software: ROS2 is used to develop software that allows robots to interact, learn new skills, and
learn from each other. ROS2 allows you to manage a network of robots, ensure their interaction in
real-time, and also provide powerful tools for development and simulation.
- Sensor systems: Install sensors to monitor the environment to help robots make decisions based
on the data they receive. Rviz and other tools and virtual simulators are used to visualize and
monitor the work of robots in real-time.
The advantage of robots working together is Swarm. Robots can be united in a swarm, which
allows them to coordinate their actions and perform tasks together, for example, when transporting
goods or performing complex manipulations. Special swarm control algorithms, such as distributed
control algorithms, are used for coordination to ensure efficiency and security.
Curricula include both hands-on activities in which students can program robots, configure their
software and test different scenarios, and research projects in which students work with the latest
robotics technologies, such as machine learning, computer vision and artificial intelligence,
particularly the LLM.
In summary, we can conclude that the described Robotics Laboratory with collaborative robots
united in a swarm is a space for innovation and research that can significantly improve the
functionality and efficiency of robotic systems in the real world. Using ROS2 as the main platform
makes this process more interactive and technological. This laboratory creates unique opportunities
for learning and development in the field of robotics.
5. Results
The development of education and the integration of advanced technologies into it indicates an
increasingly strong integration of artificial intelligence (AI) and robotics into educational
frameworks. Such convergence not only expands the capabilities of educational robots, but also
creates a more dynamic learning environment. AI-driven features such as advanced vision systems
and natural language processing will enable robots to perform a wider range of tasks, thereby
enriching the learning experience for students of all abilities and learning styles. The
implementation of IoRT/IoMT and artificial intelligence is particularly promising for creating a
more accessible learning environment for students with disabilities and promoting a more inclusive
environment.
The future of education involving robotics and the Internet of Things points to improved
technologies that facilitate self-directed learning. Research shows that approaches that give students
access to programming interfaces encourage a trial-and-error approach to learning. This not only
promotes deeper immersion in the learning activity, but also minimizes off-task behavior, resulting
in more effective learning outcomes.
6. Conclusion and Future Directions
Future research should continue to investigate effective learning gains produced by robotic
interventions, particularly through studies that track student outcomes over longer periods. Such
research will further improve assessment methodologies and provide a deeper understanding of
how educational robotics, IoRT, IoMT can improve learning in a variety of learning domains. The
results of current research serve as a fundamental basis for the design and conduct of these future
evaluations, ensuring that the impact of educational robotics on learning outcomes can be
thoroughly evaluated and understood.
7. Declaration on Generative AI
During the preparation of this article, the authors used ChatGPT in order to grammar and
spelling check. After using this tool, the authors reviewed and edited the content as needed and
takes full responsibility for the publication’s content.
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