Arman
Anzanpour
Links
Areas of expertise
Biography
I am a Postdoctoral Researcher in the Health Technology Lab within the Department of Computing at the University of Turku. I completed my PhD in Information and Communication Technology at the same university. My work lies at the intersection of computer science, electronic engineering, and clinical healthcare, with a focus on developing practical technologies for real-world health monitoring.
My primary research centers on IoT-based health monitoring systems, edge and fog computing architectures, and remote patient monitoring solutions for home environments. I am particularly motivated by the engineering aspects of this work, specializing in the design and implementation of complete IoT system loops, from raw data acquisition and secure data transmission to real-time analysis and response.
Over the past decade, I have contributed to research, development, and system design across several major projects: NewLife, RM4Health, Moore4Medical, APPLAUSE, SAFE, IoCT-CARE, and SPA. My current work focuses on the EU-funded NewLife project, where I develop smart IoT-based data systems tailored for maternal and newborn health monitoring.
To date, my research has resulted in more than 40 peer-reviewed publications, which have collectively received over 3,000 citations. I have been honored with three Best Paper Awards at international conferences, as well as Research Excellence Awards from the Nokia Foundation and the Finnish Foundation for Technology Promotion (TES), recognizing my contributions to remote health monitoring system design and development.
Teaching
Research
- Healthcare Internet of Things (IoT) and Internet of Medical Things (IoMT)
- Edge and fog computing architectures for real-time medical data processing
- Medical Early Warning Systems (EWS) and predictive healthcare algorithms
- Design and development of medical wearables and wireless sensor networks
- Bio-signal acquisition and processing
- Energy optimization and resource management in resource-constrained embedded systems
- Data science and big data engineering for high-frequency biomedical signals
- Maternal and newborns health monitoring solutions