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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Author: search.filters.author.Gonçalves, Norberto Jorge

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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
    ;
    Albuquerque, Carlos
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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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    Feasibility analysis of an electrogastrography sensor for digestion detection
    (IEEE, 2023-03-20)
    Neves, Paulo Alexandre
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    Gonçalves, Norberto Jorge
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    Varanda, Pedro
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    Simões, João
    ;
    Pires, Filipe
    This paper presents a preliminary study on the use of an electrogastrography sensor for digestion detection and monitoring. The chosen architecture is based on the BITalino platform, with a three-lead sensor array and a data acquisition and communication board. Data is gathered through Bluetooth with a computer running OpenSignals software. Although currently a process to evaluate feasibility of other devices for dietary practice is not defined, this study focusses on analysis of detection, social acceptability, comfort, and battery life. The first results seem promising, with clear detection of stomach digestion movements. However, a more refined solution must be faced in order to improve on comfort and acceptability. Future work includes data processing and logging to help detect abnormal digestion patterns and the relation with nutrition quality.
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    Corrigendum to “Extraction of notable points from ECG data: A description of a dataset related to 30-s seated and 30-s stand up”[Data in Brief, volume 46 (2023) 108874]
    (Elsevier, 2023-04-01)
    Duarte, Rui Pedro
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    Marinho, Francisco Alexandre
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    Bastos, Eduarda Sofia
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    Pinto, Rui João
    ;
    Silva, Pedro Miguel
    This work is funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/50008/2020. This work is also funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/00308/2020. Hanna Vitaliyivna Denysyuk is funded by the Portuguese Foundation for Science and Technology under scholarship number 2021.06685. BD
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    Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
    (Elsevier, 2023-02-01)
    Vitaliyivna Denysyuk, Hanna
    ;
    Pinto, Rui Joao
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    Silva, Pedro Miguel
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    Duarte, Rui Pedro
    ;
    Marinho, Francisco Alexandre
    The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.
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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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    A Brief Review on Gender Identification with Electrocardiography Data
    (MDPI, 2022-08-16)
    Bastos, Eduarda Sofia
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    Duarte, Rui Pedro
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    Marinho, Francisco Alexandre
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    Rudenko, Roman
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    Vitaliyivna Denysyuk, Hanna
    Cardiac diseases have increased over the years; thus, it is essential to predict their possible signs. Accurate prediction efficiently treats the patient’s medical history before the attack occurs. Sensors available in commonly used devices may strive for the proper and early identification of various cardiac diseases. The primary purpose of this review is to analyze studies related to gender discretization based on data from different sensors including electrocardiography and echocardiography. The analyzed studies were published between 2010 and 2022 in various scientific databases, including PubMed Central, Springer, ACM, IEEE Xplore, MDPI, and Elsevier, based on the analysis of different cardiovascular diseases. It was possible to verify that most of the analyzed studies measured similar parameters as traditional methods including the QRS complex and other waves that characterize the various individuals.
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    Monitoring of Cardiovascular Diseases: An Analysis of the Mobile Applications Available in the Google Play Store
    (MDPI, 2022-06-15)
    Vitaliyivna Denysyuk, Hanna
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    Amado, João
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    Gonçalves, Norberto Jorge
    ;
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    Garcia, Nuno M
    Cardiovascular diseases have always been here, but there has been an increase in their numbers over time. Even though there are in the digital world a few applications to help with this kind of problem, there are not enough to fulfill the needs of the patients. This study reviews mobile applications that allow patients to monitor and report cardiovascular diseases. It presents a review of 14 mobile applications that were free to download in Portugal and classified and compared according to their characteristics. The selection criteria combined the following keywords: “patient”, “cardiac/or heart”, “report”, and (“tracking” or “monitoring”). Based on the analysis, we point out the errors of the applications and present some solutions. To finish, we investigated how mobile applications can help patients track and self-report cardiovascular diseases.