A Natural Language Query Interface for Searching Personal Information on Smartwatches
This work addresses the problem of accessing personal quantified-self data on wearable devices for users, but it is incremental as it builds on existing natural language interfaces for new data types.
The paper tackles the lack of support for quantified-self queries in personal assistant systems by developing a lightweight natural language query interface for smartwatches, based on a user study analyzing textual questions to process such queries.
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.