Journal article
Deep Learning for RFID-Based Activity Recognition
Proceedings of the ... International Conference on Embedded Networked Sensor Systems. International Conference on Embedded Networked Sensor Systems, v 2016
Nov 2016
PMID: 30381808
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.
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126 citations in Scopus
Details
- Title
- Deep Learning for RFID-Based Activity Recognition
- Creators
- Xinyu Li - Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USAYanyi Zhang - Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USAIvan Marsic - Department of Electrical and Computer Engineering, Rutgers University, New Brunswick, NJ, USAAleksandra Sarcevic - College of Computing and Informatics, Drexel University, Philadelphia, PA, USARandall S Burd - Division of Trauma and Burn Surgery, Children’s National Medical Center, Washington, D.C., USA
- Publication Details
- Proceedings of the ... International Conference on Embedded Networked Sensor Systems. International Conference on Embedded Networked Sensor Systems, v 2016
- Publisher
- Association for Computing Machinery
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-85007107436
- Other Identifier
- 991014877686004721