Open AccessSmartphones as Sleep Duration Sensors: Validation of the iSenseSleep Algorithm
Author(s) -
Matteo Ciman,
Katarzyna Wac
Publication year - 2019
Publication title -
jmir mhealth and uhealth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.356
H-Index - 50
ISSN - 2291-5222
DOI - 10.2196/11930
Subject(s) - smartwatch , sleep (system call) , duration (music) , computer science , sleep patterns , actigraphy , psychology , wearable computer , audiology , medicine , circadian rhythm , embedded system , art , literature , operating system , neuroscience
Background Smartphones are becoming increasingly ubiquitous every day; they are becoming more assimilated into our everyday life, being the last thing used before going to sleep and the first one after waking up. This strong correlation between our lifestyle choices and smartphone interaction patterns enables us to use them as sensors for sleep duration assessment to understand individuals’ lifestyle and sleep patterns. Objectives The objective of this study was to estimate sleep duration based on the analysis of the users’ ON-OFF interaction with their smartphone alone using the iSenseSleep algorithm. Methods We used smartwatch sleep assessment data as the ground truth. Results were acquired with 14 different subjects collecting smartwatch and smartphone interaction data for up to 6 months each. Results Results showed that based on the smartphone ON-OFF patterns, individual’s sleep duration can be estimated with an average error of 7% (24/343) [SD 4% (17/343)] min of the total duration), enabling an estimate of sleep start and wake-up times as well as sleep deprivation patterns. Conclusions It is possible to estimate sleep duration patterns using only data related to smartphone screen interaction.