NDLI: Deep neural network for RFID-based activity recognition

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Content Provider	ACM Digital Library
Author	Burd, Randall S. Yang, JaeWon Li, Mengzhu Marsic, Ivan Li, Xinyu Zhang, Yanyi
Abstract	We propose a Deep Neural Network (DNN) structure for RFID-based activity recognition. RFID data collected from several reader antennas with overlapping coverage have potential spatiotemporal relationships that can be used for object tracking. We augmented the standard fully-connected DNN structure with additional pooling layers to extract the most representative features. For model training and testing, we used RFID data from 12 tagged objects collected during 25 actual trauma resuscitations. Our results showed 76% recognition micro-accuracy for 7 resuscitation activities and 85% average micro-accuracy for 5 resuscitation phases, which is similar to existing system that, however, require the user to wear an RFID antenna.
Starting Page	24
Ending Page	26
Page Count	3
File Format	PDF EPUB
ISBN	9781450342551
DOI	10.1145/2987354.2987355
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Deep neural network Max pooling Activity recognition Rfid
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Deep neural network for RFID-based activity recognition