Vasileios Papapanagiotou is a PhD student in the Multimedia Understanding Group (MUG) of the Information Processing Laboratory (IPL), Department of Electrical and Computers Engineering, Aristotle University of Thessaloniki (AUTh). He received his diploma from the same Department in 2013. His research interests are digital signal processing, wearable sensors, behavioural monitoring and analysis, supervised and semi-supervised machine learning, and concept-based image retrieval
2014-today – PhD candidate, Aristotle University of Thessaloniki, GreeceThesis: Signal processing of multiple wearable sensors for behavioural analysis related to eating disorders and obesity.
2013 – Diploma of Electrical and Computer Engineering, Aristotle University of Thessaloniki, GreeceThesis: Semi-supervised classifier training for content-based image retrieval from samples with label uncertainty Grade: 7.17/10.
2013-2016 – Teaching Assistant, Department of Electrical and Computer Engineering / Aristotle University of Thessaloniki, GreeceCourse: Multimedia Systems and Virtual Reality.
2013-2017 – Teaching Assistant, Department of Electrical and Computer Engineering / Aristotle University of Thessaloniki, GreeceCourse: Digital Image Processing.
2013-2017 – Research associate, Aristotle University of Thessaloniki, GreeceProjects:
BigO – Big data against Obesity, H2020-727688.
SPLENDID – Personalised Guide for Eating and Activity Behaviour for the Prevention of Obesity and Eating Disorders, FP7-610749. Developed algorithms that processing raw signal captured by the SPLENDID system’s sensors, in particular (a) an audio sensor, (b) a photoplethysmography sensor, (c) a triaxial accelerometry sensor, and (d) the Mandometer. The algorithms detect eating/snacking sessions in free-living conditions, as well as eating patterns during a mandometer-meal session. Furthermore, the final versions of the algorithms were implemented for the Android operating system.