Personalized Non-contact Sleep Stage Estimation with Weighted Probability Estimation by Ultradian Rhythm

This paper focused on ultradian rhythms (a sleep cycle of approximately 60 to 120 minute) for personalizing sleep stage estimation, and proposed a personalized sleep stage estimation method that weights the results estimated by machine learning with the predicted ultradian rhythms. The ultradian rhythms are predicted by the body movement density which is correlated with ultradian rhythm. To investigate the effectiveness of the proposed method, this paper conducts human subjects experiment for eight subjects.