Pasi Liljeberg
Professor, Head of Department
pasi.liljeberg@utu.fi +358 29 450 2469 +358 40 543 3722 Vesilinnantie 5 Turku : 1st floor |
Biomedical engineering, Internet of Things, edge computing, Wearable sensors, Digital health technology, Health data analytics, Approximate and adaptive computing,
Research interest fall to the areas of biomedical engineering, health technology and edge computing. Please see also: https://healthtech.utu.fi
Pasi Liljeberg received MSc and PhD degrees in information and communication technology from the University of Turku, Turku, Finland, in 1999 and 2005, respectively. He received Adjunct professorship in embedded computing architectures in 2010. Currently he is working as a full professor in University of Turku in the Digital Health Technology unit. At the same time he serves as head of the Department of Computing, Faculty of Technology, University of Turku. His research interests are biomedical engineering, Internet of Things, edge computing, approximate and adaptive computing, wearable sensors, digital health technology and health data analytics. Liljeberg is the (co-)author of around 300 peer-reviewed publications.
My research interest fall in the field of biomedical engineering, health technology and Internet-of-Things. This is in the context wearable biomedical, wearable technology, applied machine learning, bio-signal processing, health informatics and edge computing. Special attention is paid to novel biomedical sensing applications, wearable computing, analytics, informatics, communication, and networking paradigms, especial focus onhealthcare and wellbeing applications.
Teaching interest in the field of Health Technology in general.
- Energy-Performance Co-Management of Mixed-Sensitivity Workloads on Heterogeneous Multi-core Systems (2021)
- Proceedings of the Asia and South Pacific Design Automation Conference
- Lightweight Photoplethysmography Quality Assessment for Real-time IoT-based Health Monitoring using Unsupervised Anomaly Detection (2021)
- Procedia Computer Science
- Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation (2021)
- Sensors
- Pain assessment tool with electrodermal activity for postoperative patients: Method validation study (2021)
- JMIR mHealth and uHealth
- Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study (2021)
- Journal of Medical Internet Research
- Pregnant women's daily patterns of well-being before and during the COVID-19 pandemic in Finland: Longitudinal monitoring through smartwatch technology (2021)
- PLoS ONE
- UBAR: User- and Battery-aware Resource Management for Smartphones (2021)
- ACM Transactions in Embedded Computing Systems
- Being ‘A Google Mom’ or Securely Monitored at Home - Perceptions of Remote Monitoring in Maternity Care (2020)
- Journal of Advanced Nursing
- Context-Aware Sensing via Dynamic Programming for Edge-Assisted Wearable Systems (2020)
- ACM Transactions on Computing for Healthcare
- Continuous 7-month Internet of Things -based monitoring of health parameters of pregnant and Postpartum Women: prospective observational feasibility study (2020)
- JMIR Formative Research
- Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography (2020)
- PLoS ONE
- Edge-Assisted Control for Healthcare Internet of Things: A Case Study on PPG-Based Early Warning Score (2020)
- ACM Transactions on the Internet of Things
- Optimizing Energy Efficiency of Wearable Sensors Using Fog‐assisted Control (2020) Fog Computing: Theory and Practice Delaram Amiri, Arman Anzanpour, Iman Azimi, Amir M. Rahmani, Pasi Liljeberg, Nikil Dutt, Marco Levorato
- Prospective Study Evaluating a Pain Assessment Tool in a Postoperative Environment: Protocol for Algorithm Testing and Enhancement (2020)
- JMIR Research Protocols
- Robust ECG R-peak detection using LSTM (2020) SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing Laitala J., Jiang M., Syrjälä E., Naeini E.K., Airola A., Rahmani A.M., Dutt N.D., Liljeberg P.
- RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems (2020)
- IEEE Access
- Sleep tracking of a commercially available smart ring and smartwatch against medical-grade actigraphy in everyday settings: instrument validation study (2020)
- JMIR mHealth and uHealth
- User-centric Resource Management for Embedded Multi-core Processors (2020)
- VLSI Design
- Acute pain intensity monitoring with the classification of multiple physiological parameters (2019)
- Journal of Clinical Monitoring and Computing
- Analysis of performance and energy consumption of wearable devices and mobile gateways in IoT applications (2019) COINS '19 Proceedings of the International Conference on Omni-Layer Intelligent Systems Nakhkash M., Gia T., Azimi I., Anzanpour A., Rahmani A., Liljeberg P.