Human motion classification by micro-doppler radar using intelligent algorithms.
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| Title: | Human motion classification by micro-doppler radar using intelligent algorithms. |
|---|---|
| Authors: | Ballen, Andres Felipe Arias1 juan.martinezq@unimilitar.edu.co, Cuesta, Edith Paola Estupiñán1 edith.estupinan@unimilitar.edu.co, Quintero, Juan Carlos Martínez1 est.andres.arias1@unimilitar.edu.co |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Feb2025, Vol. 15 Issue 1, p455-466. 12p. |
| Subjects: | Continuous wave radar, Signal generators, Image recognition (Computer vision), Convolutional neural networks, Human mechanics, Motion, Radio frequency |
| Abstract: | This article introduces a technique for detecting four human movements using micro-doppler radar and intelligent algorithms. Micro-doppler radar exhibits the capability to detect and measure object movements with intricate detail, even capturing complex or non-rigid motions, while accurately identifying direction, velocity, and motion patterns. The application of intelligent algorithms enhances detection efficiency and reduces false alarms by discerning subtle movement patterns, thereby facilitating more accurate detection and a deeper understanding of observed object dynamics. A continuous wave radar setup was implemented utilizing a spectrum analyzer and radio frequency (RF) generator capturing signals in a spectrogram centered at 2,395 MHz. Six models were assessed for image classification: VGG-16, VGG-19, MobileNet, MobileNet V2, Xception, and Inception V3. A dataset comprising 500 images depicting four movements-running, walking, arm raising, and jumping-was curated. Our findings reveal that the most optimal architecture in terms of training time, accuracy, and loss is VGG-16, achieving an accuracy of 96%. Furthermore, precision values of 96%, 100%, and 98% were obtained for the movements of walking, running, and arm raising, respectively. Notably, VGG-16 exhibited a training loss of 4.191E-04, attributed to the utilization of the Adam optimizer with a learning rate of 0.001 over 15 epochs and a batch size of 32. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 181102277 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Human motion classification by micro-doppler radar using intelligent algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ballen%2C+Andres+Felipe+Arias%22">Ballen, Andres Felipe Arias</searchLink><relatesTo>1</relatesTo><i> juan.martinezq@unimilitar.edu.co</i><br /><searchLink fieldCode="AR" term="%22Cuesta%2C+Edith+Paola+Estupiñán%22">Cuesta, Edith Paola Estupiñán</searchLink><relatesTo>1</relatesTo><i> edith.estupinan@unimilitar.edu.co</i><br /><searchLink fieldCode="AR" term="%22Quintero%2C+Juan+Carlos+Martínez%22">Quintero, Juan Carlos Martínez</searchLink><relatesTo>1</relatesTo><i> est.andres.arias1@unimilitar.edu.co</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Feb2025, Vol. 15 Issue 1, p455-466. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Continuous+wave+radar%22">Continuous wave radar</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+generators%22">Signal generators</searchLink><br /><searchLink fieldCode="DE" term="%22Image+recognition+%28Computer+vision%29%22">Image recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Human+mechanics%22">Human mechanics</searchLink><br /><searchLink fieldCode="DE" term="%22Motion%22">Motion</searchLink><br /><searchLink fieldCode="DE" term="%22Radio+frequency%22">Radio frequency</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This article introduces a technique for detecting four human movements using micro-doppler radar and intelligent algorithms. Micro-doppler radar exhibits the capability to detect and measure object movements with intricate detail, even capturing complex or non-rigid motions, while accurately identifying direction, velocity, and motion patterns. The application of intelligent algorithms enhances detection efficiency and reduces false alarms by discerning subtle movement patterns, thereby facilitating more accurate detection and a deeper understanding of observed object dynamics. A continuous wave radar setup was implemented utilizing a spectrum analyzer and radio frequency (RF) generator capturing signals in a spectrogram centered at 2,395 MHz. Six models were assessed for image classification: VGG-16, VGG-19, MobileNet, MobileNet V2, Xception, and Inception V3. A dataset comprising 500 images depicting four movements-running, walking, arm raising, and jumping-was curated. Our findings reveal that the most optimal architecture in terms of training time, accuracy, and loss is VGG-16, achieving an accuracy of 96%. Furthermore, precision values of 96%, 100%, and 98% were obtained for the movements of walking, running, and arm raising, respectively. Notably, VGG-16 exhibited a training loss of 4.191E-04, attributed to the utilization of the Adam optimizer with a learning rate of 0.001 over 15 epochs and a batch size of 32. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v15i1.pp455-466 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 455 Subjects: – SubjectFull: Continuous wave radar Type: general – SubjectFull: Signal generators Type: general – SubjectFull: Image recognition (Computer vision) Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Human mechanics Type: general – SubjectFull: Motion Type: general – SubjectFull: Radio frequency Type: general Titles: – TitleFull: Human motion classification by micro-doppler radar using intelligent algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ballen, Andres Felipe Arias – PersonEntity: Name: NameFull: Cuesta, Edith Paola Estupiñán – PersonEntity: Name: NameFull: Quintero, Juan Carlos Martínez IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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