Periodicity detection in lifelog data with missing and irregularly sampled data

Lifelogging is the ambient, continuous digital recording of a person's everyday activities for a variety of possible applications. Much of the work to date in lifelogging has focused on developing sensors, capturing information, processing it into events and then supporting event-based access to the lifelog for applications like memory recall, behaviour analysis or similar. With the recent arrival of aggregating platforms such as Apple's HealthKit, Microsoft's HealthVault and Google's Fit, we are now able to collect and aggregate data from lifelog sensors, to centralize the management of data and in particular to search for and detect patterns of usage for individuals and across populations. In this paper, we present a framework that detects both low-level and high-level periodicity in lifelog data, detecting hidden patterns of which users would not otherwise be aware. We detect periodicities of time series using a combination of correlograms and periodograms, using various signal processing algorithms. Periodicity detection in lifelogs is particularly challenging because the lifelog data itself is not always continuous and can have gaps as users may use their lifelog devices intermittingly. To illustrate that periodicity can be detected from such data, we apply periodicity detection on three lifelog datasets with varying levels of completeness and accuracy.