Head of the BMI Lab
- Tsiknakis Manolis, Ph.D., Visiting Research Professor, Computational BioMedicine Lab, FORTH
Software Engineers
- Maniadi Evangelia, M.Sc.
PhD Candidates
- Chatzaki Roula, M.Sc.
- Research topic: “A multimodal approach for the assessment of mental stress based on the analysis of physiological signals”
- Chatzimina Maria, M.Sc.
- Research topic: “Conversational agents as a new diagnostic tool: a proof-of-concept study in the field of palliative care“
- Gkikas Stefanos, M.Sc.
- Research topic: “A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods”
- Skaramagkas Vasileios, BEng – MEng
- Research topic: “Early diagnosis of Parkinson’s disease symptoms based on multimodal data and Deep Machine Learning methods“
Postgraduate students
- Froudas Michail, B. Sc.
- Thesis: “Biosignal Analysis Methods for the Assessment of Stress“
- Description: The purpose of the thesis is a) to compare results obtained from different physiological signals (electrocardiogram, electrodermal activity, respiration, etc.) and different sensors (traditional sensors and wearable sensors), b) to employ various feature analysis techniques, addressing issues related to the extraction of physiological signals, such as calibration, scaling, data loss, and missing values c) to develop machine learning algorithms for multiclass classification, and d) to investigate the benefits of stress detection in the biomedical domain.
Undergraduate students
- Katis Apostolos
- Thesis: “Development and evaluation of a multilingual mobile voice recognition application“
- Kremydas Anastasios
- Thesis: “Stress recognition through Electrocardiogram signal analysis“
- Ladianos Antonios
- Thesis: “Design and implementation of an Android/Flutter app for monitoring patients’ symptoms in chronic diseases“
- Venianakis Ilias
- Thesis: “Design and implementation of an Android/Flutter app for monitoring and recording treatment for patients with chronic diseases“
- Zamagias Michail Anargyros
- Thesis: “Design and implementation of an app for emotion tracking for mobile devices (Android & iOS)“