Search Results for author: Anastasios Delopoulos

Found 13 papers, 2 papers with code

Leveraging Unlabelled Data in Multiple-Instance Learning Problems for Improved Detection of Parkinsonian Tremor in Free-Living Conditions

no code implementations29 Apr 2023 Alexandros Papadopoulos, Anastasios Delopoulos

Yet for large scale studies, obtaining even the necessary coarse ground-truth is not trivial, as a complete neurological evaluation is required.

Multiple Instance Learning

Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds

no code implementations31 Aug 2022 Aristotelis Ballas, Vasileios Papapanagiotou, Anastasios Delopoulos, Christos Diou

The PhysioNet 2022 challenge targets automatic detection of murmur from audio recordings of the heart and automatic detection of normal vs. abnormal clinical outcome.

Self-Supervised Learning

Chewing Detection from Commercial Smart-glasses

no code implementations11 Aug 2022 Vasileios Papapanagiotou, Anastasia Liapi, Anastasios Delopoulos

Automatic dietary monitoring has progressed significantly during the last years, offering a variety of solutions, both in terms of sensors and algorithms as well as in terms of what aspect or parameters of eating behavior are measured and monitored.

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

1 code implementation8 Sep 2021 Athanasios Kirmizis, Konstantinos Kyritsis, Anastasios Delopoulos

In particular, leave-one-subject-out (LOSO) experiments reveal an F1-score of 0. 863 for the detection of puffs and an F1-score/Jaccard index equal to 0. 878/0. 604 towards the temporal localization of smoking sessions during the day.

Event Detection Temporal Localization

Bite-Weight Estimation Using Commercial Ear Buds

no code implementations2 Aug 2021 Vasileios Papapanagiotou, Stefanos Ganotakis, Anastasios Delopoulos

While automatic tracking and measuring of our physical activity is a well established domain, not only in research but also in commercial products and every-day life-style, automatic measurement of eating behavior is significantly more limited.


Self-Supervised Feature Learning of 1D Convolutional Neural Networks with Contrastive Loss for Eating Detection Using an In-Ear Microphone

1 code implementation2 Aug 2021 Vasileios Papapanagiotou, Christos Diou, Anastasios Delopoulos

A common bottleneck for developing and training machine learning algorithms is obtaining labeled data for training supervised algorithms, and in particular ground truth annotations.

Image Classification

Recognition of food-texture attributes using an in-ear microphone

no code implementations20 May 2021 Vasileios Papapanagiotou, Christos Diou, Janet van den Boer, Monica Mars, Anastasios Delopoulos

Our approach performs very well in recognizing crispiness (0. 95 weighted accuracy on new subjects and 0. 93 on new food types) and demonstrates promising results for objective and automatic recognition of wetness and chewiness.


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

no code implementations12 Oct 2020 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.

Temporal Localization

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

no code implementations6 May 2020 Alexandros Papadopoulos, Konstantinos Kyritsis, Lisa Klingelhoefer, Sevasti Bostanjopoulou, K. Ray Chaudhuri, Anastasios Delopoulos

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 intensity increases as the disease progresses.

Multiple Instance Learning Navigate

Span error bound for weighted SVM with applications in hyperparameter selection

no code implementations17 Sep 2018 Ioannis Sarafis, Christos Diou, Anastasios Delopoulos

Experiments on 14 benchmark data sets and data sets with importance scores for the training instances show that: (a) the condition for the existence of span in weighted SVM is satisfied almost always; (b) the span-rule is the most effective method for weighted SVM hyperparameter selection; (c) the span-rule is the best predictor of the test error in the mean square error sense; and (d) the span-rule is efficient and, for certain problems, it can be calculated faster than $K$-fold cross-validation.

Learning Local Feature Aggregation Functions with Backpropagation

no code implementations26 Jun 2017 Angelos Katharopoulos, Despoina Paschalidou, Christos Diou, Anastasios Delopoulos

This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem).

General Classification

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