Pasi Liljeberg
Professor, Head of Department
pasi.liljeberg@utu.fi +358 29 450 2469 +358 40 543 3722 Vesilinnantie 5 Turku Työhuone: 1st floor ORCID-tunniste: https://orcid.org/0000-0002-9392-3589 |
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.
- A Deep Learning-based PPG Quality Assessment Approach for Heart Rate and Heart Rate Variability (2023)
- ACM Transactions on Computing for Healthcare
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - An energy-efficient semi-supervised approach for on-device photoplethysmogram signal quality assessment (2023)
- Smart Health
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Can Sleep Quality Attributes be Predicted from Physical Activity in Everyday Settings? (2023)
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Comparing prenatal and postpartum stress among women with previous adverse pregnancy outcomes and normal obstetric histories: A longitudinal cohort study (2023)
- Sexual & Reproductive Healthcare
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - DynaFuse: Dynamic Fusion for Resource Efficient Multi-Modal Machine Learning Inference (2023)
- IEEE Embedded Systems Letters
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Edge-Centric Optimization of Multi-modal ML-Driven eHealth Applications (2023) Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Use Cases and Emerging Challenges Kanduri Anil, Shahhosseini Sina, Naeini Emad Kasaeyan, Alikhani Hamidreza, Liljeberg Pasi, Dutt Nikil, Rahmani Amir M.
(A3 Vertaisarvioitu kirjan tai muun kokoomateoksen osa) - End-to-End PPG Processing Pipeline for Wearables: From Quality Assessment and Motion Artifacts Removal to HR/HRV Feature Extraction (2023)
- Proceedings (IEEE International Conference on Bioinformatics and Biomedicine)
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study (2023)
- JMIR Formative Research
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Personalized and adaptive neural networks for pain detection from multi-modal physiological features (2023)
- Expert Systems with Applications
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Personalized Graph Attention Network for Multivariate Time-series Change Analysis: A Case Study on Long-term Maternal Monitoring (2023) SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing Wang Yuning, Azimi Iman, Feli Mohammad, Rahmani Amir M., Liljeberg Pasi
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa)



