Areas of expertise
Arman Anzanpour is a researcher at the “Health Technology” group, Department of Computing, University of Turku. He is currently a Ph.D. candidate studying IoT-based remote health monitoring systems with a focus on resource management. He received his Master in Biomedical Engineering from Amirkabir University of Technology. His research fields include medical early warning systems, Internet of Things, fog computing, remote patient monitoring, medical wearables, and embedded electronics. Collaborating with several projects and research groups, he is practically engaged with the engineering design and implementation of wireless sensor networks, wearable devices, IoT system architectures, mobile/web application development, and biosignals acquisition systems.
- System Engineering Labs, Teacher for the IoT section, University of Turku, 2021
- Medical Instrumentation, Teaching Assistant of Tero Koivisto, University of Turku, 2020 & 2021
- IoT Systems: Design And Applications, Teaching Assistant of Dr. Matti Kaisti, University of Turku, 2019
- IoT Systems: Design And Applications, Teaching Assistant of Prof. Pasi Liljeberg, University of Turku, 2019
- Embedded IoT Programming, Teaching Assistant of Prof. Pasi Liljeberg, University of Turku, 2017
- Cyber-Physical Systems, Teaching Assistant of Prof. Amir Mohammad Rahmani, University of Turku, 2015 & 2016
- Introduction to Raspberry Pi board, University of Turku, 2015
- DELTA 2015 Summer School on Internet of Things for Health, Teaching Assistant of Prof. Amir Mohammad Rahmani and Dr. Rajeev Kumar Kanth, University of Turku, 2015
- Fundamentals of Web Design, Ferdowsi University of Mashhad, Art Faculty, 2010
- PHP: Web Programming Language, College of Ferdowsi University of Mashhad, 2004
- Visual Basic Programming, College of Ferdowsi University of Mashhad, 2003
- MATLAB Programming, College of Ferdowsi University of Mashhad, 2003
- Application of Computer in Material Science, Teaching Assistant of Prof. Ahad Zabet, Ferdowsi University of Mashhad, Engineering Faculty, 2003
A patient monitoring procedure, named Early Warning Score, is in-use in hospitals for the prevention of most sudden deterioration and has been proven to reduce the risk of consequential death or disability by 75%. The aim of this research is to implement the same life-saving technique for chronic in-home patients which requires performing the same in-hospital medical measurements via a wearable device worn by in-home patients during their daily activities. Out of the hospital, the main challenges are the management of the resources and the accuracy of the measurements. A certain level of intelligence, self-awareness, and context-awareness is required in a remote patient monitoring system to maximize the continuity of the monitoring and deal with such challenges. This research is based on an IoT technology that enables biomedical and context sensing in the sensor layer, local processing and notifications in the fog layer, and data storage, processing, and artificial intelligence in the cloud layer.