Florenc Demrozi
Professor
Kontakt
E-post: florenc.demrozi@uis.no
Rom: KE E-442
Sted: Stavanger
Organisasjonsenhet
Det teknisk- naturvitenskapelige fakultet
Institutt for data- og elektroteknologi
Kort om meg
I am currently an Professor in Medical Technology at the Department of Electrical Engineering and Computer Science, University i Stavanger, Norway, working on Human Activity Recognition (HAR), Ambient Assisted Living (AAL), Internet of Medical Things (IoMT), Sensors and Measurements.
I obtained my Ph.D. in Computer Science in May 2020 supervised by Prof. Graziano Pravadelli, with a thesis on "An IoT-based Virtual Coaching System for Assisting Activities of Daily Life".
Previously, I received my Master's degree (M.Sc.) in Computer Science and Engineering at the University of Verona in 2016 with a thesis on "Automatic generation of self-adaptive transactors from PSL assertions" under the supervision of Prof. Graziano Pravadelli.
I received my Bachelor's degree (B.Sc.) in Computer Science at the University of Verona in 2014.
From April 2021, I co-founded the IoT for Care (IoT4Care) research group at the Department of Computer Science, University of Verona, Italy. The IoT4Care group focuses on developing Virtual Coaching Systems based on IoT infrastructure and the Extended Mind concept, whose primary purpose is to help people with special needs in carrying out Activities of Daily Life (ADL's).
Currently taught courses:
Teaching competences:
- Basic course in higher education pedagogies (150 hours, 2022-2023)
- PhD-supervisory qualification program (100 hours, 2025)
Dette forsker jeg på
I specialize in AI-driven sensing systems for healthcare, focusing on Multimodal AI, Human Activity Recognition (HAR), Ambient Assisted Living (AAL), and the Internet of Medical Things (IoMT). My research integrates machine learning, wearable and IoT-based sensing, and digital health technologies to enhance personalized healthcare, independent living, and well-being.
Research Areas & Solutions
I have developed AI-powered solutions for:
🔹 Multimodal AI in Healthcare – integrating physiological, behavioral, and environmental data for advanced health monitoring.
🔹 Wearable & IoT-based health monitoring – designing low-cost, non-invasive systems for tracking motor function, gait patterns, and physiological health metrics in real-time.
🔹 Movement disorder analysis – developing AI-driven tools for Parkinson’s disease (freezing of gait detection, tremor analysis), Alzheimer's disease (cognitive and behavioral monitoring), and chemotherapy-induced peripheral neuropathy (CIPN monitoring).
🔹 Personalized virtual coaching – creating smart coaching platforms for elderly individuals, people with disabilities, and rehabilitation patients, supporting Activities of Daily Living (ADLs) through sensor-based feedback systems.
🔹 WiFi Sensing & Radio-based Monitoring – leveraging wireless signals to track indoor movement, activity patterns, and falls, offering a non-intrusive approach to health monitoring.
Collaborations & Impact
I actively collaborate with clinical experts, AI scientists, engineers, and healthcare providers across Europe and the U.S. Key collaborations include:
✅ Hospitals & Medical Institutions: Stavanger University Hospital, University of Florida (Intelligent Health Lab), Politecnico di Torino (Wearable Sensor Research).
✅ Industry & Research Networks: Norwegian Research Council, AWS Fellowship, TU Chemnitz (AI for human sensing), Brain Research Foundation Verona.
✅ Interdisciplinary Research: Cross-sector projects integrating biomedical engineering, AI, and IoT technologies for smart healthcare solutions.
Technology & Innovation
🔹 Wearable Systems for Parkinson’s Disease – a low-cost Body Area Network for real-time movement tracking and Freezing of Gait (FoG) prediction.
🔹 WiFi Sensing for Non-Invasive Monitoring – utilizing radio signals to track movement, falls, and activity levels in healthcare environments.
🔹 SHPIA 2.0 Smart Home Platform – a scalable, multi-purpose IoT system for remote monitoring and virtual coaching in assisted living.
Through these innovations, I aim to bridge the gap between AI, IoT, and real-world healthcare needs, improving early diagnosis, continuous monitoring, and patient-centered care.
Akademisk bakgrunn
I hold a Ph.D. in Computer Science from the University of Verona, Italy (2020), where I developed an IoT-based Virtual Coaching System for assisting individuals in Activities of Daily Living (ADLs). My doctoral research focused on Human Activity Recognition (HAR), Ambient Assisted Living (AAL), and the Internet of Medical Things (IoMT), integrating wearable sensors and AI-driven monitoring systems.
Prior to that, I earned a Master’s degree (M.Sc.) in Computer Science and Engineering (2016) with a thesis on automatic generation of self-adaptive TLM protocols, and a Bachelor’s degree (B.Sc.) in Computer Science (2014), both from the University of Verona.
Throughout my academic journey, I have conducted international research visits at:
✅ University of Florida, USA – Research on AI-powered healthcare monitoring at the Intelligent Health Lab.
✅ TU Chemnitz, Germany – AI-driven Human Activity Recognition (HAR) based on radio signal sensing.
✅ University of Limerick, Ireland – AWS Fellowship on AI-driven software engineering and immersive learning.
✅ University of Verona, Italy – Visiting Professor in Biomedical Engineering and AI for Healthcare Applications.
Currently, I serve as an Professor in Medical Technology at the University of Stavanger, Norway, where I focus on AI-enhanced sensing systems for healthcare, movement disorder analysis, and IoT-based health monitoring.