Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach

arXiv:2402.17601v1 Announce Type: new
Abstract: Understanding sleep and activity patterns plays a crucial role in physical and mental health. This study introduces a novel approach for sleep detection using weakly supervised learning for scenarios where reliable ground truth labels are unavailable. The proposed method relies on a set of weak labels, derived from the predictions generated by conventional sleep detection algorithms. Introducing a novel approach, we suggest a novel generalised non-linear statistical model in which the number of weak sleep …

abstract arxiv cs.lg detection health labels mental health modelling novel patterns role sleep study supervised learning truth type understanding