Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks

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dc.citation.issue19
dc.citation.volume22
dc.contributor.authorKonings D
dc.contributor.authorAlam F
dc.contributor.authorFaulkner N
dc.contributor.authorde Jong C
dc.coverage.spatialSwitzerland
dc.date.available2022-09-23
dc.date.available2022-09-20
dc.date.issued23/09/2022
dc.description.abstractIn recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.
dc.description.publication-statusPublished online
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/36236306
dc.identifiers22197206
dc.identifier.citationSensors (Basel), 2022, 22 (19)
dc.identifier.doi10.3390/s22197206
dc.identifier.eissn1424-8220
dc.identifier.elements-id456917
dc.identifier.harvestedMassey_Dark
dc.identifier.urihttps://hdl.handle.net/10179/17759
dc.languageeng
dc.publisherMDPI AG
dc.relation.isPartOfSensors (Basel)
dc.relation.urihttps://www.mdpi.com/1424-8220/22/19/7206
dc.subjectbiometrics
dc.subjectcapacitive floor
dc.subjectgender classification
dc.subjecthuman sensing
dc.subjectmachine learning
dc.subjectneural network
dc.subjectAlgorithms
dc.subjectHumans
dc.subjectMachine Learning
dc.subjectNeural Networks, Computer
dc.subjectSupport Vector Machine
dc.subject.anzsrc0301 Analytical Chemistry
dc.subject.anzsrc0805 Distributed Computing
dc.subject.anzsrc0906 Electrical and Electronic Engineering
dc.subject.anzsrc0502 Environmental Science and Management
dc.subject.anzsrc0602 Ecology
dc.titleIdentity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Food and Advanced Technology
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