  We present a testbed for exploring novel smart refrigerator interactions, and
identify three key adoption-limiting interaction shortcomings of
state-of-the-art smart fridges: lack of 1) user experience focus, 2)
low-intrusion object recognition and 2) automatic item position detection. Our
testbed system addresses these limitations by a combination of sensors,
software filters, architectural components and a RESTful API to track
interaction events in real-time, and retrieve current state and historical data
to learn patterns and recommend user actions. We evaluate the accuracy and
overhead of our system in a realistic interaction flow. The accuracy was
measured to 83-88% and the overhead compared to a representative
state-of-the-art barcode scanner improved by 27%. We also showcase two
applications built on top of our testbed, one for finding expired items and
ingredients of dishes; and one to monitor your health. The pattern that these
applications have in common is that they cast the interaction as an
item-recommendation problem triggered when the user takes something out. Our
testbed could help reveal further user-experience centric interaction patterns
and new classes of applications for smart fridges that inherently, by relying
on our testbed primitives, mitigate the issues with existing approaches.
