Long-term self-supervised learning for accelerometer-based sleep-wake recognition

Sleep is a crucial health metric linked to various health problems. Accurate analysis of sleep duration relies on identifying sleep and wake phases. While Polysomnography is considered the gold standard for sleep measurements, it is impractical for large population-based studies. Accelerometers offer an alternative but face challenges inaccurate sleep-wake recognition (SWR). Recent advances in self-supervised learning (SSL) have shown promise in related fields. This study introduces a contribution to the field of artificial intelligence by proposing anew SSL model, long-term accelerometer to vector (LTA2V), for SWR. LTA2V learns context information during pre-training using global positional encoding and long-term sequences. Experiments on three datasets show that LTA2V outperforms other methods, achieving an average F1-score of 73.8%, outperforming the best supervised method by 3.4% and the best alternative SSL model by 1.7%. Analysis of Variance and Tukey's Honest Significant Difference tests show that SSL methods significantly outperform rule-based and traditional machine learning methods and are on par with supervised deep learning. Despite improvements, SWR performance enhancements through SSL are limited compared to other fields, suggesting that there is limited scope for further major improvements in accelerometer-based SWR. Applying LTA2V in population-based studies can lead to more reliable data on sleep duration across populations, improving our understanding of how sleep impacts health.