  The importance of sleep is paramount for maintaining physical, emotional and
mental wellbeing. Though the relationship between sleep and physical activity
is known to be important, it is not yet fully understood. The explosion in
popularity of actigraphy and wearable devices, provides a unique opportunity to
understand this relationship. Leveraging this information source requires new
tools to be developed to facilitate data-driven research for sleep and activity
patient-recommendations.
  In this paper we explore the use of deep learning to build sleep quality
prediction models based on actigraphy data. We first use deep learning as a
pure model building device by performing human activity recognition (HAR) on
raw sensor data, and using deep learning to build sleep prediction models. We
compare the deep learning models with those build using classical approaches,
i.e. logistic regression, support vector machines, random forest and adaboost.
Secondly, we employ the advantage of deep learning with its ability to handle
high dimensional datasets. We explore several deep learning models on the raw
wearable sensor output without performing HAR or any other feature extraction.
  Our results show that using a convolutional neural network on the raw
wearables output improves the predictive value of sleep quality from physical
activity, by an additional 8% compared to state-of-the-art non-deep learning
approaches, which itself shows a 15% improvement over current practice.
Moreover, utilizing deep learning on raw data eliminates the need for data
pre-processing and simplifies the overall workflow to analyze actigraphy data
for sleep and physical activity research.
