Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks
Object detection Through-the-Walls enables localization and identification of hidden objects behind the walls. While numerous studies have exploited Channel State Information of Multiple Input Multiple Output (MIMO) WiFi and radar devices in association with Artificial Intelligence based algorithms...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9780155/ |
| _version_ | 1811336975155724288 |
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| author | Sameer Ahmad Bhat Muneer Ahmad Dar Piotr Szczuko Dalia Alyahya Farhana Mustafa |
| author_facet | Sameer Ahmad Bhat Muneer Ahmad Dar Piotr Szczuko Dalia Alyahya Farhana Mustafa |
| author_sort | Sameer Ahmad Bhat |
| collection | DOAJ |
| description | Object detection Through-the-Walls enables localization and identification of hidden objects behind the walls. While numerous studies have exploited Channel State Information of Multiple Input Multiple Output (MIMO) WiFi and radar devices in association with Artificial Intelligence based algorithms (AI) to detect and localize objects behind walls, this study proposes a novel non-invasive Through-the-Walls human motion direction prediction system based on a Single-Input-Single-Output (SISO) communication channel model and Shallow Neural Network (SNN). The motion direction prediction accuracy of SNN is highlighted against the other types of Machine Learning (ML) models. The comparative analysis of models in this study shows that unique human movement patterns, superimposed on received pilot radio signal, can be classified precisely by SNN, with an accuracy of approximately 89.13% compared to the other ML based models. The results of this study would guide scholars, active in developing human motion recognition systems, intrusion detection systems, or Well-being and healthcare systems, and in processes that innovate and improve processing techniques for monitoring and control. |
| first_indexed | 2024-04-13T17:47:21Z |
| format | Article |
| id | doaj.art-d0c73a7bbd0d41f8963fef6b63fd8f1d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T17:47:21Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d0c73a7bbd0d41f8963fef6b63fd8f1d2022-12-22T02:36:53ZengIEEEIEEE Access2169-35362022-01-0110568235684410.1109/ACCESS.2022.31772739780155Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural NetworksSameer Ahmad Bhat0https://orcid.org/0000-0001-9396-7760Muneer Ahmad Dar1Piotr Szczuko2Dalia Alyahya3Farhana Mustafa4Department of Multimedia Systems, Gdańsk University of Technology, Gdańsk, PolandDepartment of Computer Science, National Institute of Electronics and Information Technology, Jammu and Kashmir, IndiaDepartment of Multimedia Systems, Gdańsk University of Technology, Gdańsk, PolandDepartment of Instructional Technology, King Saud University, Riyadh, Saudi ArabiaDepartment of Electronics and Instrumentation Technology, University of Kashmir, Jammu and Kashmir, IndiaObject detection Through-the-Walls enables localization and identification of hidden objects behind the walls. While numerous studies have exploited Channel State Information of Multiple Input Multiple Output (MIMO) WiFi and radar devices in association with Artificial Intelligence based algorithms (AI) to detect and localize objects behind walls, this study proposes a novel non-invasive Through-the-Walls human motion direction prediction system based on a Single-Input-Single-Output (SISO) communication channel model and Shallow Neural Network (SNN). The motion direction prediction accuracy of SNN is highlighted against the other types of Machine Learning (ML) models. The comparative analysis of models in this study shows that unique human movement patterns, superimposed on received pilot radio signal, can be classified precisely by SNN, with an accuracy of approximately 89.13% compared to the other ML based models. The results of this study would guide scholars, active in developing human motion recognition systems, intrusion detection systems, or Well-being and healthcare systems, and in processes that innovate and improve processing techniques for monitoring and control.https://ieeexplore.ieee.org/document/9780155/Artificial intelligenceartificial neural networksclassification algorithmsdata analysisfeature extractionfeedforward neural networks |
| spellingShingle | Sameer Ahmad Bhat Muneer Ahmad Dar Piotr Szczuko Dalia Alyahya Farhana Mustafa Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks IEEE Access Artificial intelligence artificial neural networks classification algorithms data analysis feature extraction feedforward neural networks |
| title | Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks |
| title_full | Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks |
| title_fullStr | Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks |
| title_full_unstemmed | Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks |
| title_short | Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks |
| title_sort | sensing direction of human motion using single input single output siso channel model and neural networks |
| topic | Artificial intelligence artificial neural networks classification algorithms data analysis feature extraction feedforward neural networks |
| url | https://ieeexplore.ieee.org/document/9780155/ |
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