Object motion detection based on passive UHF RFID tags using a hidden Markov model-based classifier

Abstract

We present an object motion detection system using backscattered signal strength of passive UHF RFID tags as a sensor for providing information on the movement and identity of work objects—important cues for activity recognition. For using the signal strength for accurate detection of object movement we propose a novel Markov model with continuous observations, RSSI preprocessor, frame-based data segmentation, and motion-transition finder. We use the change of backscattered signal strength caused by tag's relocation to reliably detect movement of tagged objects. To maximize the accuracy of movement detection, an HMM-based classifier is designed and trained for dynamic settings, and the frequency of transitions between stationary/moving states that is characteristic for different object types. We deployed a RFID system in a hospital trauma bay and evaluated our approach with data recorded in the trauma room during 28 simulated resuscitations performed by trauma teams. Our motion detection system shows 89.5% accuracy in this domain.