Agenda

12-12:30 - Lunch

12:30 - 13:20 - Seminar

Location: A401, School of Engineering

Abstract

It is common knowledge that the population around the world is getting older and it is aging faster than ever as shown in e.g. the recent WHO Study on global ageing and adult health (SAGE). Due to the prevalence of chronic diseases, the elderly population requires an extended range of healthcare services, placing a significant burden on the healthcare workforce. This situation gives rise to various economic, technical, and clinical challenges. There is a growing anticipation regarding the potential of smart technologies, particularly the application of artificial intelligence, to enhance the health and well-being of the elderly. Researchers are addressing these challenges through two primary approaches: focusing on personalized care at the individual level to slow down the aging process and implementing effective health policies and public health interventions at the population level to mitigate the aging process and prevent escalating, often unsustainable, costs. Measurements, monitoring, and data collection form the basis for a significant portion of the information that is being entered into patient health records and decision-making systems. The quality of the input data has a big influence on the efficacy of the interventions and accuracy of analytical outcomes. Patients are required care of themselves with practically no or little supervision from medical professionals. For home use of medical devices, patients have to manage wholly or partially the devices. There will be more medical devices for home use and with time, these devices will grow in complexity (take as example use of ventilators at home during COVID pandemics). Therefore, the design of the devices needs to be reconsidered to make the devices users centred. Technological and digital literacy will be required to at least a certain level form the patients. Usability of medical devices for home use presents a challenge for home users, but to their designers as well, at least at a long run. In recent years, we have contributed to shaping some policy documents on healthy aging within the EU, though their implementation was slowed down by the COVID pandemics.

Our research is recently devoted to improving connectivity architecture and methods for IoT and wearable devices, development of an eco-system for management and self-management of diabetes and handling missing data in CGM records, segmentation and classification of human movements during exercising and rehabilitation and optimizing human fall detection circuits and algorithms. We have been working on development of reliable and trustworthy machine learning based algorithms for fall detection, and distinction between activities of daily living and falls. The research involved analysis of various biomedical signals (accelerometer, altimeter, gyroscope) acquired from healthy subjects in order to evaluate their influence on algorithm’s performance. We are looking to expand our research with the introduction of electrodermal activity (EDA) signals describing galvanic skin response, as well as photoplethysmogram (PPG) signals for calculating heart rate. These additional signals should enable us to expand our research to focus on the assessment of patient’s neurological state, adding cognitive and emotional stresses to already mentioned physical stress.

Biographies

Ratko Magjarević received his Ph.D. in Electrical Engineering in 1994 from the University of Zagreb, Faculty of Electrical Engineering. After his appointment in

industry at the Institute of Electrical Engineering “Koncar,“ he joined the Electronic Measurement and Biomedical Engineering Group at the University of Zagreb Faculty of Electrical Engineering and Computing. He is full professor teaching several courses in Electronic Instrumentation and Biomedical Engineering at undergraduate, graduate and at postgraduate studies.

His scientific and professional interest is in fields of electronic and biomedical instrumentation and digital health, in particular in cardiac potentials analysis and pacing, in research of new methods for drug delivery based on electropermeabilisation and recently in research of personalized intelligent mobile health systems. He is author or co-author of numerous journal and conference papers, several textbooks and book chapters. R. Magjarevic is elected for President of the International Federation for Medical and Biological Engineering (IFMBE) from 2022 to 2025.

Krunoslav Jurčić received his master’s degree in the field of Information processing at the Faculty of Electrical Engineering and Computing, University of Zagreb in 2021, after successfully defending his thesis “Physical Activity Recognition Based on Machine Learning”. His area of research includes signal processing and artificial intelligence, especially in the field of biomedicine. He is currently a research assistant at University of Zagreb, focusing on analysis of various biological data acquired from wearable sensors as part of the project "AI in Biomedicine". Apart from university, Krunoslav is also an active member of International Federation of Medical and Biological Engineering (IFMBE). His work with the organization includes being IFMBE Students & Recent Graduates Working Group coordinator for Europe region, as well as also being IFMBE Web Content Administrator.