Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality
Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring…
Continue Reading