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.