activity
20202025
most citedDetecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning

78 citations · 83 across the 3 of their papers we have counts for

collaborators

5 papers

eess.SP2025

Estimation of Food Intake Quantity Using Inertial Signals from Smartwatches

Ioannis Levi, Konstantinos Kyritsis, Vasileios Papapanagiotou +2

Accurate monitoring of eating behavior is crucial for managing obesity and eating disorders such as bulimia nervosa. At the same time, existing methods rely on multiple and/or spec…

cs.HC2022★ 5 cited

Intake Monitoring in Free-Living Conditions: Overview and Lessons we Have Learned

Christos Diou, Konstantinos Kyritsis, Vasileios Papapanagiotou +1

The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, inc…

eess.SP2021

A Bottom-up method Towards the Automatic and Objective Monitoring of Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors

Athanasios Kirmizis, Konstantinos Kyritsis, Anastasios Delopoulos

The consumption of tobacco has reached global epidemic proportions and is characterized as the leading cause of death and illness. Among the different ways of consuming tobacco (e.…

eess.SP2020

A Data Driven End-to-end Approach for In-the-wild Monitoring of Eating Behavior Using Smartwatches

Konstantinos Kyritsis, Christos Diou, Anastasios Delopoulos

The increased worldwide prevalence of obesity has sparked the interest of the scientific community towards tools that objectively and automatically monitor eating behavior. Despite…

eess.SP2020★ 78 cited

Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning

Alexandros Papadopoulos, Konstantinos Kyritsis, Lisa Klingelhoefer +3

Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose…