Kianoosh Kazemi
Doctoral Researcher, Health Technology
kianoosh.k.kazemi@utu.fi Medisiina D 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
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
- 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.
(Vertaisarvioitu artikkeli konferenssijulkaisussa (A4)) - 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
(Vertaisarvioitu artikkeli konferenssijulkaisussa (A4)) - 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)
(Vertaisarvioitu artikkeli konferenssijulkaisussa (A4)) - A comprehensive accuracy assessment of Samsung smartwatch heart rate and heart rate variability (2022)
- PLoS ONE
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)) - PPG Signal Reconstruction Using Deep Convolutional Generative Adversarial Network (2022)
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society
(Vertaisarvioitu artikkeli konferenssijulkaisussa (A4)) - Robust PPG Peak Detection Using Dilated Convolutional Neural Networks (2022)
- Sensors
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))
