Symbolic Object Localization Through Active Sampling of Acceleration and Sound Signatures

Abstract

We describe a novel method for symbolic location discovery of simple objects. The method requires no infrastructure and relies on simple sensors routinely used in sensor nodes and smart objects (acceleration, sound). It uses vibration and short, narrow frequency ’beeps’ to sample the response of the environment to mechanical stimuli. The method works for specific locations such as ’on the couch’, ’in the desk drawer’ as well as for location classes such as ’closed wood compartment’ or ’open iron surface’. In the latter case, it is capable of generalizing the classification to locations the object has not seen during training. We present the results of an experimental study with a total of over 1200 measurements from 35 specific locations (taken from 3 different rooms) and 12 abstract location classes. It includes such similar locations as the inner and outer pocket of a jacket and a table and shelf made of the same wood. Nonetheless on locations from a single room (16 in the largest one) we achieve a recognition rate of up to 96 %. It goes down to 81 % if all 35 locations are taken together, however the correct location is in the 3 top picks of the system 94 % of the times.KeywordsSensor NodeConfusion MatrixFusion MethodUbiquitous ComputingSmart ObjectThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.