Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device.

ABSTRACT: Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and "clock proxy") to performance across several classifiers. Best performance was achieved using ne

SUBMITTER: Walch O 

PROVIDER: S-EPMC6930135 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature