  Sleep plays a vital role in our physical, cognitive, and psychological
well-being. Despite its importance, long-term monitoring of personalized sleep
quality (SQ) in real-world contexts is still challenging. Many sleep researches
are still developing clinically and far from accessible to the general public.
Fortunately, wearables and IoT devices provide the potential to explore the
sleep insights from multimodal data, and have been used in some SQ researches.
However, most of these studies analyze the sleep related data and present the
results in a delayed manner (i.e., today's SQ obtained from last night's data),
it is sill difficult for individuals to know how their sleep will be before
they go to bed and how they can proactively improve it. To this end, this paper
proposes a computational framework to monitor the individual SQ based on both
the objective and subjective data from multiple sources, and moves a step
further towards providing the personalized feedback to improve the SQ in a
data-driven manner. The feedback is implemented by referring the insights from
the PMData dataset based on the discovered patterns between life events and
different levels of SQ. The deep learning based personal SQ model (PerSQ),
using the long-term heterogeneous data and considering the carry-over effect,
achieves higher prediction performance compared with baseline models. A case
study also shows reasonable results for an individual to monitor and improve
the SQ in the future.
