Sleep is a composite of physiological and
behavioral processes that undergo substantial changes during and after
pregnancy. These changes might lead to sleep disorders and adverse pregnancy
outcomes. Several studies have investigated this issue; however, they were
restricted to subjective measurements or short-term actigraphy methods. This is
insufficient for a longitudinal maternal sleep quality evaluation. A
longitudinal study a) requires a long-term data collection approach to
acquire data from everyday routines of mothers and b) demands a sleep
quality assessment method exploiting a large volume of multivariate data to
assess sleep adaptations and overall sleep quality. In this paper, we present
an Internet-of-Things based long-term monitoring system to perform an objective
sleep quality assessment. We conduct longitudinal monitoring where 20 pregnant
mothers are remotely monitored for six months of pregnancy and one month
postpartum. To evaluate sleep quality adaptations, we a) extract several
sleep attributes and study their variations during the monitoring and b)
propose a semi-supervised machine learning approach to create a personalized
sleep model for each subject. The model provides an abnormality score which
allows an explicit representation of the sleep quality in a clinical routine,
reflecting possible sleep quality degradation with respect to her own data.
Sleep data of 13 participants (out of 20) are included in our analysis, as
their data are adequate for the study, including 172.15 ± 33.29 days of sleep data per person. Our fine-grained objective
measurements indicate the sleep duration and sleep efficiency are deteriorated
in pregnancy and notably in postpartum. In comparison to the mid of the second
trimester, the sleep model indicates the increase of sleep abnormality at the
end of pregnancy (2.87 times) and postpartum (5.62 times). We also show the
model enables individualized and effective care for sleep disturbances during
pregnancy, as compared to a baseline method.