A methodology for developing quality of information metrics for body sensor design

Body sensors networks (BSNs) are emerging technologies that are enabling long-term, continuous, remote monitoring of physiologic and biokinematic information for various medical applications. Because of the varying computational, storage, and communication capabilities of different components in the BSN, system designers must make design choices that trade off information quality with resource consumption and system battery lifetime. Given these trade-offs, there is the possibility that the information presented to the health practitioner at the end point may deviate from what was originally sensed. In some cases, these deviations may cause a practitioner to make a different decision from what would have been made given the original data. Engineers working on such systems typically resort to traditional measures of data quality like RMSE; however, these metrics have been shown in many cases to not correlate well with the notions of information quality for the particular application. Objective metrics of information distortion and its effects on decision making are therefore necessary to help BSN designers make more informed trade-offs between design constraints and information quality and to help practitioners understand the kind of information being produced by BSNs, on which they have to base decisions. In this paper, we present a general methodology for developing such metrics for various BSN applications, illustrate how this methodology can be applied to a real application through a case study, and discuss issues with developing such metrics.