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Currently, personal assistant systems, run on smartphones and use naturallanguage interfaces. However, these systems rely mostly on the web for findinginformation. Mobile and wearable devices can collect an enormous amount ofcontextual personal data such as sleep and physical activities. Theseinformation objects and their applications are known as quantified-self, mobilehealth or personal informatics, and they can be used to provide a deeperinsight into our behavior. To our knowledge, existing personal assistantsystems do not support all types of quantified-self queries. In response tothis, we have undertaken a user study to analyze a set of "textualquestions/queries" that users have used to search their quantified-self ormobile health data. Through analyzing these questions, we have constructed alight-weight natural language based query interface, including a text parseralgorithm and a user interface, to process the users' queries that have beenused for searching quantified-self information. This query interface has beendesigned to operate on small devices, i.e. smartwatches, as well as augmentingthe personal assistant systems by allowing them to process end users' naturallanguage queries about their quantified-self data.

A Natural Language Query Interface for Searching Personal Information on Smartwatches