activity
20182021
most citedSelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data

129 citations · 190 across the 4 of their papers we have counts for

collaborators

6 papers

eess.AS20212 cited

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

Björn W. Schuller, Anton Batliner, Christian Bergler +21

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the CO…

cs.LG2021129 cited

SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data

Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis +3

Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition. However, the performance of such models in real-wo…

cs.LG20203 cited

Learning Generalizable Physiological Representations from Large-scale Wearable Data

Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage +2

To date, research on sensor-equipped mobile devices has primarily focused on the purely supervised task of human activity recognition (walking, running, etc), demonstrating limited…

cs.LG202056 cited

Exploring Contrastive Learning in Human Activity Recognition for Healthcare

Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis +1

Human Activity Recognition (HAR) constitutes one of the most important tasks for wearable and mobile sensing given its implications in human well-being and health monitoring. Motiv…

cs.SD2020

Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data

Chloë Brown, Jagmohan Chauhan, Andreas Grammenos +6

Audio signals generated by the human body (e.g., sighs, breathing, heart, digestion, vibration sounds) have routinely been used by clinicians as indicators to diagnose disease or a…

cs.CV2018

Interactive dimensionality reduction using similarity projections

Dimitris Spathis, Nikolaos Passalis, Anastasios Tefas

Recent advances in machine learning allow us to analyze and describe the content of high-dimensional data like text, audio, images or other signals. In order to visualize that data…