Fingerprinting was used next and data was gathered.  Offline training data was collected by the device and then fed into two different machine learning algorithms.  The first was a k Nearest Neighbor algorithm.  The researchers found that with this algorithm, the location of the device was placed within the correct section of the room 62.7% of the time.  This indicates that there was an average of 2-3 meters shift in radius from the actual position of the device.