Faculty of Computer Science and Engineering

Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5

The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest faculties in that field in the region. The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.

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Ten Meter Walk Test for motor function assessment with technological devices based on lower members’ movements: A systematic review
(Elsevier BV, 2025-03)
Santos, Maykol
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Albuquerque, Carlos
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Coelho, Paulo Jorge
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Pires, Ivan Miguel
Objective: The Ten Meter Walk Test (10MWT) is a vital diagnostic tool for identifying neuromuscular and neurodegenerative conditions. This systematic review explores the potential of wearables, mobile devices, and sensors to enhance the 10MWT’s use in medical gait analysis based on lower limb movements. Methods: This systematic review explores the use of wearables, mobile devices, and sensors to improve the 10MWT in medical gait analysis based on lower limb movements. The study uses the PRISMA approach to assess literature from January 2010 to October 2023, highlighting the importance of new technologies like machine learning and artificial intelligence in improving the accuracy and efficiency of the 10MWT. Results: The findings demonstrate how technology-enabled 10MWT can help develop specialized treatment strategies and provide a more accurate understanding of disease pathophysiology. Conclusions: The paper reviews 17 studies on lower limb movements during the 10MWT, highlighting their importance in assessing medical diseases and gait analysis as a diagnostic tool. It emphasizes the role of technology in rehabilitation and physical therapy, where some studies combine Transcranial Direct Current Stimulation with robotic or wearable technologies. Significance: The review comprehensively explains these technologies’ advantages and current use in therapeutic contexts.
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Overcoming technical barriers in healthcare with blockchain: A systematic review
(Elsevier BV, 2026-03)
Costa, Diana
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Coelho, Paulo Jorge
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Albuquerque, Carlos
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Pires, Ivan Miguel
The rising digitalization of healthcare has increased reliance on complex information systems, creating the need for better integration and interoperability. Despite technology developments, healthcare organizations still confront technical challenges that restrict data transmission, scalability, and secure information sharing. This systematic review highlights important technological challenges to healthcare information system integration and examines the potential of blockchain technology to address them. Following PRISMA principles, a structured search of PubMed and Scopus revealed 24 peer-reviewed studies published from 2020 to 2024. The investigation suggests that interoperability restrictions, lack of data and language standardization, scalability challenges, cybersecurity hazards, and insufficient technical expertise are the most significant hurdles. Evidence suggests that blockchain technology can increase data integrity, security, and regulated interoperability through decentralized and permissioned systems. However, obstacles persist involving technical complexity, regulatory compliance, energy consumption, and organizational readiness. This paper highlights current knowledge on technical integration hurdles in healthcare and presents evidence-based insights on the potential role of blockchain in allowing interoperable, safe, and sustainable healthcare information systems.
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Technological methods for sensors’ data analysis for Y-balance test results: A systematic review
(Elsevier BV, 2026-07)
Pimenta, Luís
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Lopes, Mário
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Al-Jumaili, Saif
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Albuquerque, Carlos
Objective To evaluate the impact of sensor integration on YBT outcomes, including data precision, injury risk prediction, real-time monitoring, and athletic performance assessment. A secondary objective is to identify gaps in automated real-time evaluation methods. Methods A systematic review was conducted using a search window covering studies published between 2020 and 2026. After screening and eligibility assessment, the final included studies were published between 2021 and 2025. This review focused on studies that either applied sensor-based or technology-assisted methods directly to Y-Balance Test assessment or used YBT as a functional outcome alongside technology-supported measurement approaches. Results After screening and eligibility assessment, 21 studies met the inclusion criteria and were included in the qualitative and descriptive synthesis. The reviewed studies suggest that technology-assisted approaches can broaden the assessment of Y-Balance Test performance by adding biomechanical, functional, or task-related information beyond conventional manual scoring. Several studies reported improved monitoring of balance-related outcomes or intervention-related changes, but direct evidence for improved measurement precision and formal injury prediction was limited. Conclusions Sensor-assisted approaches in YBT show promising potential to improve measurement objectivity and broaden functional assessment in clinical and athletic settings. However, the current literature does not yet demonstrate a fully automated or real-time YBT system, and further development is required before such applications can be considered established for routine practice. Future progress will require larger and more diverse cohorts, methodological standardization, robust validation procedures, and the development of portable real-time YBT-specific systems suitable for routine implementation. Significance This review contributes a structured evidence map of sensor-assisted YBT research and highlights the gap between existing technology-supported assessment approaches and truly automated, real-time, YBT-specific systems.
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Use of machine learning for predicting stress episodes based on wearable sensor data: A systematic review
(Elsevier BV, 2025-11)
Pataca, António Oseas
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Coelho, Paulo Jorge
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Garcia, Nuno M.
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Deryuck, Margot
Objective: This study consists of a systematic literature review that aims to explore the potential of integrating wearable sensor data and machine learning (ML) techniques for predicting stress episodes. It aims to identify prevalent sensors, key physiological features, and the effectiveness of ML methods in real-world stress monitoring and prediction. Methods: This systematic review follows the PRISMA methodology, analyzing literature from January 2010 to June 2025. Data sources included IEEE Xplore, Elsevier, Springer, Multidisciplinary Digital Publishing Institute (MDPI), and paper repositories such as PubMed Central and the Association of Computing Machinery (ACM). The inclusion criteria encompassed studies that employed wearable devices for ML stress prediction, focusing on physiological data such as heart rate variability (HRV), skin conductance, and sleep patterns. Articles were screened for originality, clinical relevance, and methodological rigor. Results: Key findings highlighted the use of diverse wearable sensors, including electrodermal activity (EDA), photoplethysmography (PPG), and accelerometers. Commonly extracted features included HRV metrics, skin conductance levels, and respiratory patterns. ML models, such as Random Forest (RF), Support Vector Machines (SVM), and deep neural networks (DNN), have demonstrated high predictive accuracy (e.g., up to 99%). Despite promising results, challenges such as small sample sizes, variability in data quality, and the need for standardized protocols were noted. Conclusion: Wearable sensors combined with ML algorithms provide scalable, real-time stress monitoring solutions, emphasizing proactive healthcare management. However, advancing this field requires addressing limitations through interdisciplinary collaboration and focusing on the accessibility and usability of technologies. Significance: This study highlights the transformative role of wearable technologies in predicting stress, with implications for personalized health interventions, mental health support, and enhanced healthcare efficiency.
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Colonoscopy image analysis for polyp detection: A systematic review of existing approaches and opportunities
(Elsevier BV, 2025)
Albuquerque, Carlos
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Neves, Paulo Alexandre
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Godinho, António
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A low-cost device-based data approach to Eight Hop Test
(Elsevier BV, 2025)
Pimenta, Luís
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Coelho, Paulo Jorge
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Gonçalves, Norberto Jorge
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Lousado, José Paulo
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Albuquerque, Carlos
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Sensor-based systems for the measurement of Functional Reach Test results: a systematic review
(PeerJ, 2024)
Francisco, Luís
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Duarte, João
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Godinho, António Nunes
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Albuquerque, Carlos
The measurement of Functional Reach Test (FRT) is a widely used assessment tool in various fields, including physical therapy, rehabilitation, and geriatrics. This test evaluates a person's balance, mobility, and functional ability to reach forward while maintaining stability. Recently, there has been a growing interest in utilizing sensor-based systems to objectively and accurately measure FRT results. This systematic review was performed in various scientific databases or publishers, including PubMed Central, IEEE Explore, Elsevier, Springer, the Multidisciplinary Digital Publishing Institute (MDPI), and the Association for Computing Machinery (ACM), and considered studies published between January 2017 and October 2022, related to methods for the automation of the measurement of the Functional Reach Test variables and results with sensors. Camera-based devices and motion-based sensors are used for Functional Reach Tests, with statistical models extracting meaningful information. Sensor-based systems offer several advantages over traditional manual measurement techniques, as they can provide objective and precise measurements of the reach distance, quantify postural sway, and capture additional parameters related to the movement.
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Can sensors be used to measure the Arm Curl Test results? a systematic review
(Springer Science and Business Media LLC, 2024-01-31)
Matos, Tomás
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Vornicoglo, Daniel
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Coelho, Paulo Jorge
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Albuquerque, Carlos
<jats:title>Abstract</jats:title><jats:p>There is growing interest in the automated measurement of physical fitness tests, such as the Arm Curl Test, to enable more objective and accurate assessments. This review aimed to systematically analyze the types of sensors and technological methods used for automated Arm Curl Test measurement and their benefits for different populations. The search consisted of the search related to the possibilities to measure the Arm Curl Test results with sensors in scientific databases, including PubMed Central, IEEE Explore, Elsevier, Springer, MDPI, ACM, and PMC, published from January 2010 to October 2022. The analysis included 30 studies from 15 nations with diverse populations analyzed. According to data extraction, the most prevalent sensors were chronometers, accelerometers, stadiometers, and dynamometers. In the investigations, statistical analysis predominated. The study shows how automated sensor technologies can objectively measure the Arm Curl Test. The detected sensors combined with statistical analysis techniques can enhance assessments. Applications for the Arm Curl Test may be improved even more with more research on cutting-edge sensors and algorithms. This evaluation offers insightful information about utilizing sensor-based automation to enhance Arm Curl Testing.</jats:p>
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Ten meter walk test with mobile devices: A dataset with accelerometer, magnetometer, and gyroscope
(Elsevier BV, 2024-02)
Gabriel, Cristiana Lopes
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Pires, Ivan Miguel
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Gonçalves, Norberto Jorge
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Coelho, Paulo Jorge
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Thought on Food: A Systematic Review of Current Approaches and Challenges for Food Intake Detection
(MDPI, 2022-08-26)
Neves, Paulo Alexandre
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Simões, João
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Costa, Ricardo
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Pimenta, Luís
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Gonçalves, Norberto Jorge
Nowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.

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