3 papers
cs.LG2026
Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks
William Lehn-Schiøler, Magnus Ruud Kjær, Phillip Hempel +7
Sleep foundation models have recently demonstrated strong performance on in-domain polysomnography tasks, including sleep staging, apnea detection, and disease risk prediction. In…
cs.LG2025
Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Models
Magnus Ruud Kjaer, Rahul Thapa, Gauri Ganjoo +7
Polysomnography (PSG), the gold standard test for sleep analysis, generates vast amounts of multimodal clinical data, presenting an opportunity to leverage self-supervised represen…
eess.SP2025
Frequency-Aware Masked Autoencoders for Human Activity Recognition using Accelerometers
Niels R. Lorenzen, Poul J. Jennum, Emmanuel Mignot +1
Wearable accelerometers are widely used for continuous monitoring of physical activity. Supervised machine learning and deep learning algorithms have long been used to extract mean…