An approach to model-aware measurements

In this paper, we spotlight the issue of considering precision and accuracy as concepts to inflate in the interpretation of measurements, and not only in the measurements themselves. Except from microscale dimensions where the Heisenberg principle can be a matter of concern, when we normally measure a physical quantity we are supposing to be truthfully compliant with the hypothesis that our instrumental equipment plus the measurement context are not affecting significantly the observed measurands. Then, by means of repeated experiments, we make use of precision and accuracy assessments to provide an estimate of the quality of the measure, i.e., its closeness to the true value. In this context, one issue that deserves attention is the influence the context has on the interpretation of the values of the measurands. This problem is strictly related to the information processing at the application level. Consciously far from claiming to provide a complete picture, we propose some reflections coming from the experience gained in the field of computational models to enhance selectivity in highly cross-sensitive sensors. The main point is that, hidden behind every form of interpretation, there is the need of a knowledge model sufficiently robust to encapsulate the multi-level granular context variables affecting the measures. Our prevision is that next generation smart sensor technologies will have to deal with such knowledge-related issues on a wider scale. Since the market is moving towards ever more “intelligent” measures, a preliminary assessment of these problems ought to be pursued.