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Capturing Features and Performing Human Detection from Human Gaits Using RFID.

Yajun Zhang1, Xu Liu1, Zhixiong Yang1

  • 1School of Software, XinJiang University, Ürümqi 830091, China.

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Summary

Radio frequency identification (RFID) sensing offers a contact-free, low-cost method for person detection. This new RF-Detection system accurately identifies multiple individuals by analyzing step length and using deep learning, achieving 98.93% accuracy.

Keywords:
RFIDdeep learningperson identificationtarget detection

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Biomedical Engineering

Background:

  • Radio frequency identification (RFID) sensing is gaining traction for its non-contact, cost-effective, and lightweight properties.
  • Existing RFID-based person detection methods struggle with multi-person identification and require extensive data processing for accurate whole-body activity analysis.
  • These limitations hinder real-time applications and scalability for detecting numerous individuals simultaneously.

Purpose of the Study:

  • To develop an efficient and accurate person detection system using RFID technology.
  • To address the limitations of current systems in handling multiple people and processing time.
  • To enhance the accuracy and applicability of RFID for real-time human detection.

Main Methods:

  • Proposed RF-Detection system utilizes step length, correlated with height, as a primary detection metric.
  • Step length is segmented into specific ranges for improved accuracy with new users after extensive training.
  • Continuous individuals are segmented to enhance multi-person detection capabilities within the same space.
  • Dataset expansion and deep learning methods were employed to reduce data collection needs and boost overall accuracy.

Main Results:

  • The RF-Detection system achieved an overall recognition accuracy of 98.93%.
  • The method demonstrates improved ability to identify numerous people concurrently.
  • The system shows high accuracy for new user detection through specific step length training.

Conclusions:

  • RF-Detection offers a significant advancement in RFID-based person detection.
  • The system effectively addresses challenges in multi-person identification and real-time processing.
  • The proposed approach demonstrates high accuracy and efficiency, paving the way for practical applications.