1 code implementation • 31 Jul 2023 • Yu Wu, Dimitris Spathis, Hong Jia, Ignacio Perez-Pozuelo, Tomas Gonzales, Soren Brage, Nicholas Wareham, Cecilia Mascolo
However, most healthcare datasets with high-quality (gold-standard) labels are small-scale, as directly collecting ground truth is often costly and time-consuming.
no code implementations • 20 Nov 2022 • Yu Wu, Dimitris Spathis, Hong Jia, Ignacio Perez-Pozuelo, Tomas I. Gonzales, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Deep learning models have shown great promise in various healthcare applications.
1 code implementation • 6 May 2022 • Dimitris Spathis, Ignacio Perez-Pozuelo, Tomas I. Gonzales, Yu Wu, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Cardiorespiratory fitness is an established predictor of metabolic disease and mortality.
1 code implementation • 11 Feb 2021 • Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, Soren Brage, Nick Wareham, Cecilia Mascolo
Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition.
1 code implementation • 23 Nov 2020 • Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, Cecilia Mascolo
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.
1 code implementation • 18 Nov 2020 • Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas J. Wareham, Cecilia Mascolo
Our contributions are two-fold: i) the pre-training task creates a model that can accurately forecast HR based only on cheap activity sensors, and ii) we leverage the information captured through this task by proposing a simple method to aggregate the learnt latent representations (embeddings) from the window-level to user-level.
2 code implementations • 9 Nov 2020 • Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas J. Wareham, Cecilia Mascolo
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 success in inferring high-level health outcomes from low-level signals, such as acceleration.