  Robots have the potential to improve health monitoring outcomes for the
elderly by providing doctors, and caregivers with information about the
person's behavior, health activities and their surrounding environment. Over
the years, less work has been done to enable robots to preserve information for
longer periods of time, on the order of months and years of data, and use this
contextual information to answer queries. Time complexity to process this
massive sensor data in a timely fashion, inability to anticipate the future
queries in advance and imprecision involved in the results have been the main
impediments in making progress in this area. We make a contribution by
introducing RoboMem, a query answering system for health-care assistance of
elderly over long term; continuous data feeds that intends to overcome the
challenges of giving long term memory to robots. The design for our framework
preprocesses the sensor data and stores this preprocessed data into the
database. This data is updated in the database by going through successive
refinements, improving its accuracy for responding to queries. If data in the
database is not enough to answer a query, a small set of relevant frames (also
obtained from the database) will be reprocessed to obtain the answer. [Our
initial prototype of RoboMem stores 3.5MB of data in the database as compared
to 535.8MB of actual video frames and with minimal data in the database it is
able to fetch information fundamental to respond to queries in 0.0002 seconds
on average].
