@inproceedings{be1883f3d2354e7993c52730f6f0e927,
title = "Multimodal ambulatory sleep detection",
abstract = "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. The method achieved a sleep/wake classification accuracy of 96.5%, and sleep episode on/offset detection F1 scores of 0.85 and 0.82, respectively, with mean errors of 5.3 and 5.5 min, respectively, when compared with sleep/wake state and sleep episode on/offset assessed using actigraphy and sleep diaries.",
keywords = "temperature measurement, Skin, Storms, temperature sensors, Smart phones, Training, sleep detection, neural network, sleep duration, sleep time",
author = "Weixuan Chen and Akane Sano and Martinez, {Daniel Lopez} and Sara Taylor and McHill, {Andrew W.} and Phillips, {Andrew J. K.} and Laura Barger and Klerman, {Elizabeth B.} and Picard, {Rosalind W.}",
year = "2017",
month = apr,
day = "13",
doi = "10.1109/BHI.2017.7897306",
language = "English",
series = "2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "465--468",
booktitle = "2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017",
address = "United States",
note = "4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 ; Conference date: 16-02-2017 Through 19-02-2017",
}