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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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
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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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Wearable and mobile data analysis methodologies for personalized medicine
(Frontiers, 2023-09-20)
Pires, Ivan Miguel
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Dobre, Ciprian
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Garcia, Nuno M
The Frontiers in Digital Health Research Topic Wearable and Mobile Data Analysis Methodologies for Personalized Medicine aimed to receive contributions related to the multidisciplinary field of personalized and precision medicine, which encompasses physics, statistics, telemedicine, biomedical engineering, digital signal processing, artificial intelligence, system engineering, and health privacy and security. Information and communication technologies have changed the landscape of many knowledge and societal areas, and medicine included. Bringing technology to the end users (or patients), a larger share of users can engage in personalized and precision therapies. Numerous and diverse pathologies can be monitored remotely, and as a consequence, better monitoring of health-related information may not only empower people but also hold the promise of aiding in the early detection of diseases.
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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
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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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Mobile and wearable technologies for the analysis of Ten Meter Walk Test: A concise systematic review
(Elsevier, 2023-05-25)
Lopes Gabriel, Cristiana
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Pires, Ivan Miguel
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Coelho, Paulo Jorge
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Physical issues started to receive more attention due to the sedentary lifestyle prevalent in modern culture. The Ten Meter Walk Test allows measuring the person’s capacity to walk along 10 m and analyzing the advancement of various medical procedures for ailments, including stroke. This systematic review is related to the use of mobile or wearable devices to measure physical parameters while administering the Ten Meter Walk Test for the analysis of the performance of the test. We applied the PRISMA methodology for searching the papers related to the Ten Meter Walk Test. Natural Language Processing (NLP) algorithms were used to automate the screening process. Various papers published in two decades from multiple scientific databases, including IEEE Xplore, Elsevier, Springer, EMBASE, SCOPUS, Multidisciplinary Digital Publishing Institute (MDPI), and PubMed Central were analyzed, focusing on various diseases, devices, features, and methods. The study reveals that chronometer and accelerometer sensors measuring spatiotemporal features are the most pertinent in the Gait characterization of most diseases. Likewise, all studies emphasized the close relation between the quality of the sensor’s data obtained and the system’s ultimate accuracy. In other words, calibration procedures are needed because of the body part where the sensor is worn and the type of sensor. In addition, using ambient sensors providing kinematic and kinetic features in conjunction with wearable sensors and consistently acquiring walking signals can enhance the system’s performance. The most common weaknesses in the analyzed studies are the sample size and the unavailability of continuous monitoring devices for measuring the Ten Meter Walk Test.
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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
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Pinto, Rui Joao
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Silva, Pedro Miguel
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Duarte, Rui Pedro
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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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CoviHealth: A pilot study with teenagers in schools of Centre of Portugal
(Springer, Cham, 2020-12-03)
Villasana, María Vanessa
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Pires, Ivan Miguel
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Sá, Juliana
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Garcia, Nuno M
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. Obesity is one of the most common problem that can be avoid with the correct education of the teenagers. There are different methods, but the use of the mobile devices to promote the creation of social challenges is important, because the teenagers act mainly in groups. The use of questionnaires, challenges and gamification purposes may promote the use of this type of mobile applications by teenagers. It is a special population that needs the adoption of different interactive technologies. The studies available are not validated by healthcare professionals. First of all, we started to analyze the related work of obesity problem, mobile applications, and different methodologies adopted with teenagers. By the end, seven students participated in the study with the performance of visualization of daily tips and curiosities, answering questionnaires, monitoring of physical activity and gamification. The teenagers were satisfied with the strategies adopted, but this study was affected by the pandemic situation around the world. In general, the participants were satisfied with the use of the mobile, and they would like to use it in the future for the improvement of their nutrition and physical activity habits.
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Control and Prevention of Personal Stress
(IEEE, 2020-12-10)
Marques, Hugo
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Carvalho, Hugo
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Morgado, José
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Garcia, Nuno M
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Pires, Ivan Miguel
Stress is or could be one of the most talked-about and recurring things in recent years, because of the world that we live. Stress is our body's response to a pressure thing or situation in our life. In this way, countless things have a stressful impact on our lives. On the other hand, stress is usually due to something new or unexpected, that somehow is beyond our control. The effects on our body are evident, inevitably having symptoms. When we are exposed to stress, certain hormones are released in our body, and the immune system is working on selfdefence. During this, breathing becomes faster, heart rate increases, muscles contract and blood pressure also increases. Thus, the organism is ready to act to protect itself. It is where our project comes in, because, with these symptoms of our body, they allow stress to be identified. This paper is focused on precisely that, because, by reading the person's vital data, we can establish standards of normality, which, when they suffer variation, may indicate to us in advance that the person is stressed and help him to control himself so that there is no more significant damage. With this, we hope to obtain positive results in people's lives and routine, causing the stress rate to drop worldwide.
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Experimental Study on Wound Area Measurement with Mobile Devices
(MDPI, 2021-08-26)
Ferreira, Filipe
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Pires, Ivan Miguel
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Ponciano, Vasco
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Costa, Mónica
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Villasana, María Vanessa
Healthcare treatments might benefit from advances in artificial intelligence and technological equipment such as smartphones and smartwatches. The presence of cameras in these devices with increasingly robust and precise pattern recognition techniques can facilitate the estimation of the wound area and other telemedicine measurements. Currently, telemedicine is vital to the maintenance of the quality of the treatments remotely. This study proposes a method for measuring the wound area with mobile devices. The proposed approach relies on a multi-step process consisting of image capture, conversion to grayscale, blurring, application of a threshold with segmentation, identification of the wound part, dilation and erosion of the detected wound section, identification of accurate data related to the image, and measurement of the wound area. The proposed method was implemented with the OpenCV framework. Thus, it is a solution for healthcare systems by which to investigate and treat people with skin-related diseases. The proof-of-concept was performed with a static dataset of camera images on a desktop computer. After we validated the approach’s feasibility, we implemented the method in a mobile application that allows for communication between patients, caregivers, and healthcare professionals.
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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.

Faculty of Computer Science and Engineering

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