Fig. 1: The digital sleep framework covers the path of sleep data from its acquisition to when its insights are used for medical or consumer applications.

The framework begins with the acquisition of sleep-related data. This can be done using a variety of sensors, ranging from polysomnography to bed sensors. This data is then stored and curated, a step that comprises privacy-aware storage, cleaning, filtering and anonymisation. Once that data has been appropriately treated, the processing step takes place whereby data is transformed and integrated based on the end-model. For example it may undergo different transformations like normalization or featurization. The next step entails modeling, which can consist of simple heuristic methods, statistical learning or deep learning methods, for example. Finally, the resulting model can be deployed for a variety either medical or consumer applications.