Multimodal ambulatory sleep detection

Weixuan Chen, Akane Sano, Daniel Lopez Martinez, Sara Taylor, Andrew W. McHill, Andrew J.K. Phillips, Laura Barger, Elizabeth B. Klerman, Rosalind W. Picard

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

13 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Subtitle of host publication4th IEEE International Conference on Biomedical and Health Informatics
Place of PublicationPiscataway, NJ
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)9781509041794
Publication statusPublished - 11 Apr 2017
Externally publishedYes
EventIEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2017 - Orlando, United States of America
Duration: 16 Feb 201719 Feb 2017
Conference number: 4th (Proceedings)


ConferenceIEEE-EMBS International Conference on Biomedical and Health Informatics (BHI) 2017
Abbreviated titleBHI 2017
Country/TerritoryUnited States of America
Internet address

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