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Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique

Version 2 2024-06-04, 04:38
Version 1 2023-02-20, 03:27
journal contribution
posted on 2024-06-04, 04:38 authored by Saud Altaf, Muhammad Haroon, Shafiq Ahmad, Emad Abouel Nasr, Mazen Zaindin, Shamsul HudaShamsul Huda, Zia ur Rehman
Human pose recognition is a new field of study that promises to have widespread practical applications. While there have been efforts to improve human position estimation with radio frequency identification (RFID), no major research has addressed the problem of predicting full-body poses. Therefore, a system that can determine the human pose by analyzing the entire human body, from the head to the toes, is required. This paper presents a 3D human pose recognition framework based on ANN for learning error estimation. A workable laboratory-based multisensory testbed has been developed to verify the concept and validation of results. A case study was discussed to determine the conditions under which an acceptable estimation rate can be achieved in pose analysis. Using the Butterworth filtering technique, environmental factors are de-noised to reduce the system’s computational cost. The acquired signal is then segmented using an adaptive moving average technique to determine the beginning and ending points of an activity, and significant features are extracted to estimate the activity of each human pose. Experiments demonstrate that RFID transceiver-based solutions can be used effectively to estimate a person’s pose in real time using the proposed method.

History

Journal

Electronics

Volume

12

Article number

ARTN 374

Pagination

374-374

ISSN

1450-5843

eISSN

2079-9292

Language

en

Publication classification

C1 Refereed article in a scholarly journal

Issue

2

Publisher

MDPI AG

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