  Commercial activity trackers are set to become an essential tool in health
research, due to increasing availability in the general population. The
corresponding vast amounts of mostly unlabeled data pose a challenge to
statistical modeling approaches. To investigate the feasibility of deep
learning approaches for unsupervised learning with such data, we examine weekly
usage patterns of Fitbit activity trackers with deep Boltzmann machines (DBMs).
This method is particularly suitable for modeling complex joint distributions
via latent variables. We also chose this specific procedure because it is a
generative approach, i.e., artificial samples can be generated to explore the
learned structure. We describe how the data can be preprocessed to be
compatible with binary DBMs. The results reveal two distinct usage patterns in
which one group frequently uses trackers on Mondays and Tuesdays, whereas the
other uses trackers during the entire week. This exemplary result shows that
DBMs are feasible and can be useful for modeling activity tracker data.

Machine Learning;Machine Learning