  Currently, personal assistant systems, run on smartphones and use natural
language interfaces. However, these systems rely mostly on the web for finding
information. Mobile and wearable devices can collect an enormous amount of
contextual personal data such as sleep and physical activities. These
information objects and their applications are known as quantified-self, mobile
health or personal informatics, and they can be used to provide a deeper
insight into our behavior. To our knowledge, existing personal assistant
systems do not support all types of quantified-self queries. In response to
this, we have undertaken a user study to analyze a set of "textual
questions/queries" that users have used to search their quantified-self or
mobile health data. Through analyzing these questions, we have constructed a
light-weight natural language based query interface, including a text parser
algorithm and a user interface, to process the users' queries that have been
used for searching quantified-self information. This query interface has been
designed to operate on small devices, i.e. smartwatches, as well as augmenting
the personal assistant systems by allowing them to process end users' natural
language queries about their quantified-self data.
