On Integrating Human Decisions with Physical Sensors for Binary Decision Making

Abstract

Allowing humans to act as soft sensors is increasingly becoming an attractive solution to enhance decision making performance when the available physical (hard) sensors are limited. While the fusion problem with hard data has a rich history, fusion of hard and soft data requires further understanding due to human related factors associated with human sensor data. In this work, we investigate how the presence of human sensors can be modeled in the statistical signal processing framework and the factors that need to be taken into account when integrating soft human sensor data with hard data in a signal detection framework. We consider two cases. In the first case, both types of sensors are assumed to make threshold based individual decisions using identical observations. While physical sensors use a fixed threshold, the thresholds used by human sensors are assumed to be random variables. With a given distribution for the random thresholds used at the human sensors, by properly designing the thresholds at the physical sensors, an enhanced detection performance can be observed in the integrated system compared to performing fusion with only physical sensors. In the second case, we evaluate the fusion performance when human sensors possess some side information regarding the phenomenon in addition to the common observations available at the two types of sensors.