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

1Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Lee, Y. H., & Marsic, I. (2018). Object motion detection based on passive UHF RFID tags using a hidden Markov model-based classifier. Sensing and Bio-Sensing Research, 21, 65–74. https://doi.org/10.1016/j.sbsr.2018.10.005

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free