Extremely Low Frequency Detection for Biometric Sensing

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dc.contributor.advisorYadid-Pecht, Orly
dc.contributor.authorWhite, Oliver
dc.contributor.committeememberDonev, Jason
dc.contributor.committeememberFear, Elise
dc.contributor.committeememberCuriel, Laura
dc.date2023-11
dc.date.accessioned2023-09-15T17:15:40Z
dc.date.available2023-09-15T17:15:40Z
dc.date.issued2023-09-10
dc.description.abstractHuman detection is an important aspect of security and surveillance and is a highly researched area, typically using cameras, infrared sensors, or radar. Extremely Low Frequency (ELF) electromagnetic waves, ranging from 0.03 Hz to 300 Hz, offer unique advantages for human detection due to their ability to penetrate various materials, including walls and obstacles. The human body generates a weak magnetic field due to various physiological processes and is measurable within the ELF range. The goal of this thesis is to detect ELF signals emitted by human subjects using an ELF magnetometer and to rapidly distinguish the presence of a human from background noise. A large air-core magnetic induction coil is designed and constructed in conjunction with a low-noise, high-impedance amplification circuit to independently measure the ELF signals from background noise and two human subjects in a semi-remote, non-shielded location. A sampling frequency of 44.1 kHz was used, and the power spectrum density (PSD) was computed for each 3-second data segment. Using a Recurrent Neural Network (RNN) structure, several binary classification models were trained with supervised learning on the PSD sequences between 0.3 and 30 Hz to distinguish background data from human data. The candidate RNN models achieved an average accuracy of above 80% using K-Fold cross-validation on the limited dataset. Overall, the performance of the RNNs was high for one subject, and low for the other subject. It was found that a significant factor in distinguishing a human from background noise was the change in diurnal ELF background power levels between measurements. The results indicate the use of ELF data from humans has the potential for rapid human detection, however, requires a larger dataset for further consideration as a biometric device.
dc.identifier.citationWhite, O. (2023). Extremely low frequency detection for biometric sensing (Master's thesis, Universty of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/117046
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectextremely low frequency (ELF)
dc.subjectmagnetometer design
dc.subjecthuman detection
dc.subjectrecurrent neural networks (RNNs)
dc.subject.classificationEducation--Sciences
dc.titleExtremely Low Frequency Detection for Biometric Sensing
dc.typemaster thesis
thesis.degree.disciplineEngineering – Electrical & Computer
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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