  Whereas it has become easier for individuals to track their personal health
data (e.g., heart rate, step count, food log), there is still a wide chasm
between the collection of data and the generation of meaningful explanations to
help users better understand what their data means to them. With an increased
comprehension of their data, users will be able to act upon the newfound
information and work towards striving closer to their health goals. We aim to
bridge the gap between data collection and explanation generation by mining the
data for interesting behavioral findings that may provide hints about a user's
tendencies. Our focus is on improving the explainability of temporal personal
health data via a set of informative summary templates, or "protoforms." These
protoforms span both evaluation-based summaries that help users evaluate their
health goals and pattern-based summaries that explain their implicit behaviors.
In addition to individual users, the protoforms we use are also designed for
population-level summaries. We apply our approach to generate summaries (both
univariate and multivariate) from real user data and show that our system can
generate interesting and useful explanations.
