Tailoring Radar-Based Patient Monitoring Models to Real-Life Needs using Utility Maximization

Abstract

Monitoring patients in hospitals or care homes using radars is an interesting problem with life-saving applications. Today deep neural networks are employed for patient monitoring, but these do not provide uncertainties, nor do they consider the asymmetry in the real life cost of misclassifying different activities. In this work we use Bayesian Neural Networks that provide uncertainty on their predictions. We combine these models with a self-defined utility function to obtain tailored predictions that are more conservative for classes where misclassifications come at a higher risk or cost. We show that Bayesian neural networks are more robust, and generalize better on radar human-activity images than deterministic ones, and that they are able to reduce the cost of misclassifications in a realistic example setting by 37% compared to approaches from literature.