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Deep Neural Network for RFID-Based Activity Recognition.

Xinyu Li1, Yanyi Zhang1, Mengzhu Li1

  • 1Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA.

Proceedings of the Eighth Wireless of the Students, by the Students, and for the Students Workshop. Workshop on Wireless of the Students, by the Students, for the Students (8Th : 2016 : New York, N.Y.)
|December 4, 2018
PubMed
Summary
This summary is machine-generated.

We developed a Deep Neural Network (DNN) for RFID-based activity recognition, achieving 76% accuracy in recognizing trauma resuscitation activities without requiring wearable antennas.

Keywords:
Activity RecognitionDeep Neural NetworkMax PoolingRFID

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Area of Science:

  • Medical Informatics
  • Computer Science
  • Signal Processing

Background:

  • Radio-Frequency Identification (RFID) systems generate spatiotemporal data useful for object tracking.
  • Existing RFID activity recognition systems often require users to wear antennas, limiting practicality.

Purpose of the Study:

  • To propose a novel Deep Neural Network (DNN) structure for enhanced RFID-based activity recognition.
  • To leverage spatiotemporal features from multiple RFID readers for improved performance.

Main Methods:

  • Augmented a standard fully-connected DNN with pooling layers for feature extraction.
  • Trained and tested the model using RFID data from 12 tagged objects during 25 trauma resuscitations.

Main Results:

  • Achieved 76% micro-accuracy for recognizing 7 distinct resuscitation activities.
  • Attained an 85% average micro-accuracy for identifying 5 resuscitation phases.
  • Demonstrated comparable performance to existing systems without the need for wearable RFID antennas.

Conclusions:

  • The proposed DNN structure effectively utilizes RFID data for accurate activity recognition in critical care settings.
  • This approach offers a practical alternative to systems requiring wearable RFID components.
  • The findings highlight the potential of DNNs for analyzing complex spatiotemporal RFID data.