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Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning
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| Zeitschriftentitel: | Sensors |
|---|---|
| Personen und Körperschaften: | , , , , |
| In: | Sensors, 20, 2020, 5, S. 1421 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
MDPI AG
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Khorshid, Ahmed E. Alquaydheb, Ibrahim N. Kurdahi, Fadi Jover, Roger Piqueras Eltawil, Ahmed Khorshid, Ahmed E. Alquaydheb, Ibrahim N. Kurdahi, Fadi Jover, Roger Piqueras Eltawil, Ahmed |
|---|---|
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Khorshid, Ahmed E. Alquaydheb, Ibrahim N. Kurdahi, Fadi Jover, Roger Piqueras Eltawil, Ahmed |
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Khorshid, Ahmed E. Alquaydheb, Ibrahim N. Kurdahi, Fadi Jover, Roger Piqueras Eltawil, Ahmed Sensors Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry |
| author_sort |
khorshid, ahmed e. |
| spelling |
Khorshid, Ahmed E. Alquaydheb, Ibrahim N. Kurdahi, Fadi Jover, Roger Piqueras Eltawil, Ahmed 1424-8220 MDPI AG Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry http://dx.doi.org/10.3390/s20051421 <jats:p>In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials’ phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification.</jats:p> Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning Sensors |
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| title |
Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
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Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
| title_full |
Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
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Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
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Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
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Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
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biometric identity based on intra-body communication channel characteristics and machine learning |
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Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry |
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http://dx.doi.org/10.3390/s20051421 |
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2020 |
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<jats:p>In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials’ phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification.</jats:p> |
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| author | Khorshid, Ahmed E., Alquaydheb, Ibrahim N., Kurdahi, Fadi, Jover, Roger Piqueras, Eltawil, Ahmed |
| author_facet | Khorshid, Ahmed E., Alquaydheb, Ibrahim N., Kurdahi, Fadi, Jover, Roger Piqueras, Eltawil, Ahmed, Khorshid, Ahmed E., Alquaydheb, Ibrahim N., Kurdahi, Fadi, Jover, Roger Piqueras, Eltawil, Ahmed |
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| description | <jats:p>In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials’ phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification.</jats:p> |
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| spelling | Khorshid, Ahmed E. Alquaydheb, Ibrahim N. Kurdahi, Fadi Jover, Roger Piqueras Eltawil, Ahmed 1424-8220 MDPI AG Electrical and Electronic Engineering Biochemistry Instrumentation Atomic and Molecular Physics, and Optics Analytical Chemistry http://dx.doi.org/10.3390/s20051421 <jats:p>In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials’ phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification.</jats:p> Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning Sensors |
| spellingShingle | Khorshid, Ahmed E., Alquaydheb, Ibrahim N., Kurdahi, Fadi, Jover, Roger Piqueras, Eltawil, Ahmed, Sensors, Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning, Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry |
| title | Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
| title_full | Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
| title_fullStr | Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
| title_full_unstemmed | Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
| title_short | Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
| title_sort | biometric identity based on intra-body communication channel characteristics and machine learning |
| title_unstemmed | Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning |
| topic | Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry |
| url | http://dx.doi.org/10.3390/s20051421 |