129 citations · 134 across the 4 of their papers we have counts for
3 papers · 1 filter
UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction
Yu Wu, Dimitris Spathis, Hong Jia +5
Deep learning models have shown great promise in various healthcare monitoring applications. However, most healthcare datasets with high-quality (gold-standard) labels are small-sc…
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…
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…