  Self-tracking can improve people's awareness of their unhealthy behaviors and
support reflection to inform behavior change. Increasingly, new technologies
make tracking easier, leading to large amounts of tracked data. However, much
of that information is not salient for reflection and self-awareness. To tackle
this burden for reflection, we created the SalienTrack framework, which aims to
1) identify salient tracking events, 2) select the salient details of those
events, 3) explain why they are informative, and 4) present the details as
manually elicited or automatically shown feedback. We implemented SalienTrack
in the context of nutrition tracking. To do this, we first conducted a field
study to collect photo-based mobile food tracking over 1-5 weeks. We then
report how we used this data to train an explainable-AI model of salience.
Finally, we created interfaces to present salient information and conducted a
formative user study to gain insights about how SalienTrack could be integrated
into an interface for reflection. Our key contributions are the SalienTrack
framework, a demonstration of its implementation for semi-automated feedback in
an important and challenging self-tracking context and a discussion of the
broader uses of the framework.
