Kianoosh Kazemi
Doctoral Researcher, Health Technology
kianoosh.k.kazemi@utu.fi Työhuone: Health Technology Group ORCID-tunniste: https://orcid.org/https://orcid.org/0000-0002-0919-8661 |
Biomedical Engineering, Digital Health Technology, Machine Learning, Bio-signal Processing
I am working at Digital Health Technology research group in Department of Computing, University of Turku.
In 2017, he received his Bachelor’s degree from Shiraz University in Electrical Engineering. In 2019, he obtained his Master’s degree from the Amirkabir University of Technology (Tehran Polytechnic), Iran in Electrical Engineering, Telecommunication. In March 2021 he joined the Department of Computing at Turku University as a researcher. He is currently a Ph.D. candidate at the University of Turku, working on smart health monitoring frameworks based on the Internet of Things and Machine Learning Approaches.
Highly interested in smart health monitoring frameworks based on the Internet of Things and Machine Learning. My research has been focused on developing data analysis methods for biomedical signals, e.g., photoplethysmogram (PPG) and electrocardiogram (ECG). I have been working with machine-learning-based health data analytic techniques, biosignal denoising and recunstruction, and PPG peak detection techniques, to mention a few.
Acquisition and Analysis of Biosignals
Biosignal Analytics
- Multitask learning approach for PPG applications: Case studies on signal quality assessment and physiological parameters estimation (2025)
- Computers in Biology and Medicine
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Respiration Rate Estimation via Smartwatch-based Photoplethysmography and Accelerometer Data: A Transfer Learning Approach (2025)
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach (2024)
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Improved sleep stage predictions by deep learning of photoplethysmogram and respiration patterns (2024)
- Computers in Biology and Medicine
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Robust CNN-based Respiration Rate Estimation for Smartwatch PPG and IMU (2024) ICBRA '23: Proceedings of the 2023 10th International Conference on Bioinformatics Research and Applications Kazemi Kianoosh, Azimi Iman, Liljeberg Pasi, Rahmani Amir M.
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - 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) - 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) - A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability (2022)
- PLoS ONE
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - PPG Signal Reconstruction Using Deep Convolutional Generative Adversarial Network (2022)
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Robust PPG Peak Detection Using Dilated Convolutional Neural Networks (2022)
- Sensors
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä )
