Deep Learning for RFID-Based Activity Recognition.

Abstract

We present a system for activity recognition from passive RFID data using a deep convolutional neural network. We directly feed the RFID data into a deep convolutional neural network for activity recognition instead of selecting features and using a cascade structure that first detects object use from RFID data followed by predicting the activity. Because our system treats activity recognition as a multi-class classification problem, it is scalable for applications with large number of activity classes. We tested our system using RFID data collected in a trauma room, including 14 hours of RFID data from 16 actual trauma resuscitations. Our system outperformed existing systems developed for activity recognition and achieved similar performance with process-phase detection as systems that require wearable sensors or manually-generated input. We also analyzed the strengths and limitations of our current deep learning architecture for activity recognition from RFID data.

• Authors: Xinyu Li, Yanyi Zhang, Ivan Marsic, Aleksandra Sarcevic, Randall S Burd
• Journal: Proceedings of the ... International Conference on Embedded Networked Sensor Systems. International Conference on Embedded Networked Sensor Systems (Proc Int Conf Embed Netw Sens Syst) Vol. 2016 Pg. 164-175 (Nov 2016) United States
• PMID: 30381808 (Publication Type: Journal Article)