Weixuan Chen, Akane Sano, Daniel Lopez Martinez, Sara Taylor, Andrew W McHill, Andrew J K Phillips, Laura Barger, Elizabeth B Klerman, Rosalind W Picard
Inadequate sleep affects health in multiple ways. Unobtrusive ambulatory methods to monitor long-term sleep patterns in large populations could be useful for health and policy decisions. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep episode on/offset. We collected 5580 days of multimodal data and applied recurrent neural networks for sleep/wake classification, followed by cross-correlation-based template matching for sleep episode on/offset detection...
February 2017: IEEE-EMBS International Conference on Biomedical and Health Informatics