Detecting Object Motion Using Passive RFID: A Trauma Resuscitation Case Study

We studied object motion detection in an indoor environment using RFID technology. Unlike prior work, we focus on dynamic scenarios, such as emergency medical situations, subject to signal interference by people and many RFID tags. We build a realistic trauma resuscitation setting and record a dataset of around 14000 detection instances. We find that factors affecting radio signal, such as tag motion, have different statistical fingerprints, making them discernible using statistical methods. Our method for object motion detection extracts descriptive features of the received signal strength and classifies them using machine-learning techniques. We report experimental results obtained with several statistical features and classifiers, and provide guidelines for feature and classifier selection in different environments. Experimental results show that object motion could be detected with an accuracy of 80% in complex scenarios and 90% on average. The motion type, on the other hand, could not be identified with such high accuracy using currently available passive RFID technology.