One of the challenges of marrying sensor data collection and machine learning algorithms to generate usable modes for accomplishing something useful is negotiating the right level of abstraction of data flowing from the sensor to the learner. If the sensor data is too low-level, it functions as little more than noise and the learning algorithm will interpret spurious random patterns as something meaningful. If the data flow is too high-level, you’ve probably wasted time and effort implementing learning infrastructure that is little more than a simple mapping from one high-level concept to another. The trick is finding the right middle ground that maximizes the usefulness of the models being generated with expending as little time and resources processing the data from a low-level noisy signal to something more meaningful.
After weeks of discussion and brainstorming, we conceded that in order to bump the semantic level of our data up a notch, Purple Robot would have to initiate an interaction with the patient to ask a few targeted questions to assist the sensor moving forward. Since this is not unlike calibrating a measuring instrument, we’ve been calling these interactions “calibrators”.