Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique
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,...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/2/374 |
| _version_ | 1797443475609747456 |
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| author | Saud Altaf Muhammad Haroon Shafiq Ahmad Emad Abouel Nasr Mazen Zaindin Shamsul Huda Zia ur Rehman |
| author_facet | Saud Altaf Muhammad Haroon Shafiq Ahmad Emad Abouel Nasr Mazen Zaindin Shamsul Huda Zia ur Rehman |
| author_sort | Saud Altaf |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T12:56:36Z |
| format | Article |
| id | doaj.art-1b81478d8fb44200b15fbf92b7d05f2c |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T12:56:36Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-1b81478d8fb44200b15fbf92b7d05f2c2023-11-30T21:59:35ZengMDPI AGElectronics2079-92922023-01-0112237410.3390/electronics12020374Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level TechniqueSaud Altaf0Muhammad Haroon1Shafiq Ahmad2Emad Abouel Nasr3Mazen Zaindin4Shamsul Huda5Zia ur Rehman6University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46300, PakistanUniversity Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46300, PakistanIndustrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaIndustrial Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi ArabiaDepartment of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi ArabiaSchool of Information Technology, Deakin University, Burwood, VIC 3128, AustraliaUniversity Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46300, PakistanHuman 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.https://www.mdpi.com/2079-9292/12/2/3743D human pose estimationRFIDfilteringkinematicANN |
| spellingShingle | Saud Altaf Muhammad Haroon Shafiq Ahmad Emad Abouel Nasr Mazen Zaindin Shamsul Huda Zia ur Rehman Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique Electronics 3D human pose estimation RFID filtering kinematic ANN |
| title | Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique |
| title_full | Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique |
| title_fullStr | Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique |
| title_full_unstemmed | Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique |
| title_short | Radio-Frequency-Identification-Based 3D Human Pose Estimation Using Knowledge-Level Technique |
| title_sort | radio frequency identification based 3d human pose estimation using knowledge level technique |
| topic | 3D human pose estimation RFID filtering kinematic ANN |
| url | https://www.mdpi.com/2079-9292/12/2/374 |
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