Identity Authentication System using a Support Vector Machine (SVM) on Radar Respiration Measurements

Islam, Shekh Md Mahmudul; Rahman, Ashikur; Prasad, N. N.; Borić–Lubecke, Olga; Lubecke, Victor M.

doi:10.1109/arftg.2019.8739240

Cited by 29 publications

(35 citation statements)

References 9 publications

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“…For further investigation, in another study, the feasibility of recognizing people after performing post-physiological activities was tested (Islam et al, 2020b). It was found that subject recognition still worked with an accuracy of 92% (Islam et al, 2019a;Islam et al, 2020b). Experimental results demonstrated that, after short exertion, dynamically segmented exhale area and breathing depth increased by more than 1.4 times for all participants, which made evident the uniqueness of the residual heart volume after expiration for recognizing each subject, even after short exertion (Islam et al, 2019a;Islam et al, 2020b).…”

Section: Radar-based Continuous Identity Authenticationmentioning

confidence: 99%

“…It was found that subject recognition still worked with an accuracy of 92% (Islam et al, 2019a;Islam et al, 2020b). Experimental results demonstrated that, after short exertion, dynamically segmented exhale area and breathing depth increased by more than 1.4 times for all participants, which made evident the uniqueness of the residual heart volume after expiration for recognizing each subject, even after short exertion (Islam et al, 2019a;Islam et al, 2020b). Furthermore, there was another investigation of identity authentication of patients with obstructive sleep apnea (OSA) symptoms based on extracting respiratory features (peak power spectral density, packing density, and linear envelop error) for radar captured paradoxical breathing patterns, in a small-scale clinical sleep study integrating three different machine learning classifiers (SVM, KNN, and random forest) .…”

Section: Radar-based Continuous Identity Authenticationmentioning

confidence: 99%

“…Prior research has demonstrated the feasibility of utilizing Doppler radar for tracking breathing rate and heart rate accurately (Lin, 1975, Islam et al, 2019aDroitcour et al, 2001;Droitcour, 2006;Li et al, 2013). Moreover, the feasibility of extracting different abnormal respiratory patterns using microwave Doppler radar has been investigated (Islam et al, 2019b;Lee et al, 2014).…”

Section: Remote Breathing Monitoringmentioning

confidence: 99%

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Can Radar Remote Life Sensing Technology Help Combat COVID-19?

2021

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COVID-19, caused by SARS-CoV-2, is now a global pandemic disease. This outbreak has affected every aspect of life including work, leisure, and interaction with technology. Governments around the world have issued orders for travel bans, social distancing, and lockdown to control the spread of the virus and prevent strain on hospitals. This paper explores potential applications for radar-based non-contact remote respiration sensing technology that may help to combat the COVID-19 pandemic, and outlines potential advantages that may also help to reduce the spread of the virus. Applications arising from recent developments in the state of the art for transceiver and signal processing technologies will be discussed along associated technical implications. These applications include remote breathing rate monitoring, continuous identity authentication, occupancy detection, and hand gesture recognition. This paper also highlights future research directions that must be explored further to bring this innovative non-contact sensor technology into real-world implementation.

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Section: Radar-based Continuous Identity Authenticationmentioning

confidence: 99%

Section: Radar-based Continuous Identity Authenticationmentioning

confidence: 99%

Section: Remote Breathing Monitoringmentioning

confidence: 99%

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Can Radar Remote Life Sensing Technology Help Combat COVID-19?

2021

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“…Based on reported results researchers have focused on extracting two different measures: respiratory-based and heart-based features [11]. The Biosensing Laboratory at the University of Hawaii at Manoa focused on a respiratory-based featurerelated identity authentication system [10,11,12,13,14], which is described here in-detail along with the feasibility of the use of heart-based features in recognizing people [11]. An illustration of a proposed unobtrusive biometric identification system based on respiration is shown in Figure 2.…”

Section: The Radar-based Non-contact Identity Authentication Systemmentioning

confidence: 99%

BreathID: Radar’s New Role in Biometrics

Islam¹

2021

Preprint

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<p> This article is focused on providing some insight into the requirements for a non-contact continuous identity authentication system using radar sensing technologies. The article also discusses the basic principles supporting the potential new role of radar sensing technologies in a broad area of applications. It also highlights challenges associated with this new technology and provides some direction on what is needed to address requirements for real-world applications. </p>

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“…Artificial intelligence (AI) has been used to recognize different targets such as text/words, expression of disease, food identification, and identity authentication system (Curtis, 1987;Anwar and Ahmad, 2016;Bai, 2017;Buss, 2018;Liu et al, 2018;Jia et al, 2019;Islam et al, 2019). Known for being efficient, accurate, consistent, and cost-effective, AI suits the meat industry's rapid mass production (Liu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts

Borhan

Zhang

et al. 2021

Front. Sens.

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The objective of this research was to evaluate the deep learning neural network in artificial intelligence (AI) technologies to rapidly classify seven different beef cuts (bone in rib eye steak, boneless rib eye steak, chuck steak, flank steak, New York strip, short rib, and tenderloin). Color images of beef samples were acquired from a laboratory-based computer vision system and collected from the Internet (Google Images) platforms. A total of 1,113 beef cut images were used as training, validation, and testing data subsets for this project. The model developed from the deep learning neural network algorithm was able to classify certain beef cuts (flank steak and tenderloin) up to 100% accuracy. Two pretrained convolution neutral network (CNN) models Visual Geometry Group (VGG16) and Inception ResNet V2 were used to train, validate, and test these models in classifying beef cut images. An image augmentation technique was incorporated in the convolution neutral network models for avoiding the overfitting problems, which demonstrated an improvement in the performance of the image classifier model. The VGG16 model outperformed the Inception ResNet V2 model. The VGG16 model coupled with data augmentation technique was able to achieve the highest accuracy of 98.6% on 116 test images, whereas Inception ResNet V2 accomplished a maximum accuracy of 95.7% on the same test images. Based on the performance metrics of both models, deep learning technology evidently showed a promising effort for beef cuts recognition in the meat science industry.

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Identity Authentication System using a Support Vector Machine (SVM) on Radar Respiration Measurements

Cited by 29 publications

References 9 publications

Can Radar Remote Life Sensing Technology Help Combat COVID-19?

Can Radar Remote Life Sensing Technology Help Combat COVID-19?

BreathID: Radar’s New Role in Biometrics

Using Deep Learning Neural Network in Artificial Intelligence Technology to Classify Beef Cuts

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