Heart ID: Human Identification Based on Radar Micro-Doppler Signatures of the Heart Using Deep Learning

Cao, Peng; Xia, Weijie; Li, Yi

doi:10.3390/rs11101220

Cited by 27 publications

(19 citation statements)

References 18 publications

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“…We can relate that to the superior accuracies achieved using the gait signature compared to other signatures, as in Ref. [ 2 ]. Additionally, a wide variety of deep learning structures are utilized for classification, with numbers of participants that range from 4 [ 33 ] to 100 [ 16 ].…”

Section: Resultsmentioning

confidence: 99%

“…Increased demand for surveillance and security has accelerated research in human identification based on remote sensing technologies [ 1 , 2 , 3 , 4 , 5 ]. Radar, as a sensing modality, offers robustness and privacy compared to other technologies [ 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, heart sounds, which can be defined as the noises generated by the beating heart and the resultant flow of blood through it, are introduced in the proposed system as a unique biometric identifier due to the differences of people’s physical and, more specifically, cardiovascular characteristics. In previous literature [ 2 , 19 ], the related yet distinguishable radar-based heartbeat signal is proposed as a biometric identifier. While both signals can be extracted from the chest displacement waveform, the main difference is that heart sounds are smaller in magnitude and higher in frequency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dual-Biometric Human Identification Using Radar Deep Transfer Learning

Alkasimi

Shepard

Wagner

et al. 2022

Sensors

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Accurate human identification using radar has a variety of potential applications, such as surveillance, access control and security checkpoints. Nevertheless, radar-based human identification has been limited to a few motion-based biometrics that are solely reliant on micro-Doppler signatures. This paper proposes for the first time the use of combined radar-based heart sound and gait signals as biometrics for human identification. The proposed methodology starts by converting the extracted biometric signatures collected from 18 subjects to images, and then an image augmentation technique is applied and the deep transfer learning is used to classify each subject. A validation accuracy of 58.7% and 96% is reported for the heart sound and gait biometrics, respectively. Next, the identification results of the two biometrics are combined using the joint probability mass function (PMF) method to report a 98% identification accuracy. To the best of our knowledge, this is the highest reported in the literature to date. Lastly, the trained networks are tested in an actual scenario while being used in an office access control platform to identify different human subjects. We report an accuracy of 76.25%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dual-Biometric Human Identification Using Radar Deep Transfer Learning

Alkasimi

Shepard

Wagner

et al. 2022

Sensors

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“…Motion artifacts and multisubject scenarios were not considered and remain a significant challenge for this approach. Recently, another study demonstrated the efficacy of radar-based identity authentication using a short-time Fourier Transform (STFT) [76]. Each person sat a 1.5 m distance and physiological signatures were recorded for about 6 seconds of the breathing pattern, using a 24-GHz continuous wave radar.…”

Section: Radar-based Identity Authentication Through Heart-based Featuresmentioning

confidence: 99%

Radar-Based Non-Contact Continuous Identity Authentication

et al. 2020

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Non-contact vital signs monitoring using microwave Doppler radar has shown great promise in healthcare applications. Recently, this unobtrusive form of physiological sensing has also been gaining attention for its potential for continuous identity authentication, which can reduce the vulnerability of traditional one-pass validation authentication systems. Physiological Doppler radar is an attractive approach for continuous identity authentication as it requires neither contact nor line-of-sight and does not give rise to privacy concerns associated with video imaging. This paper presents a review of recent advances in radar-based identity authentication systems. It includes an evaluation of the applicability of different research efforts in authentication using respiratory patterns and heart-based dynamics. It also identifies aspects of future research required to address remaining challenges in applying unobtrusive respiration-based or heart-based identity authentication to practical systems. With the advancement of machine learning and artificial intelligence, radar-based continuous authentication can grow to serve a wide range of valuable functions in society.

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“…Respiration rate (RR) and heart rate (HR) are critical physiological parameters for human beings, especially with respect to medical applications [1]- [3], life detection [4], and public safety [5]. Methods for obtaining the information can be divided into contact and noncontact measurements.…”

Section: Introductionmentioning

confidence: 99%

Noncontact Heart Rate Measurement Based on an Improved Convolutional Sparse Coding Method Using IR-UWB Radar

Wang¹,

Qi²,

Liu³

et al. 2019

IEEE Access

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Impulse radio ultrawideband radar is a critical remote sensing tool for life detection and noncontact monitoring of vital signals. In noncontact monitoring via radar, the disturbance from respiration and environmental noise is considered critical for the estimation of heart rates. However, the heartbeat signal is generally distorted by breath harmonics and fluctuations in the time domain, and the frequencies of the vital signals are closely situated; thus, it is difficult to employ an ordinary frequency filter for separation. To solve this problem, a novel method was developed to extract heartbeat information. In this study, convolutional sparse coding, which is an unsupervised machine learning algorithm, was first used to model the heartbeat signal in the time domain, given the respiration and relative artifacts. The proposed scheme was then used to decompose the time-domain signals and directly obtain the heartbeat component by exploiting the sparsity of the heartbeat signal in the time domain. Furthermore, the performance of the proposed scheme was improved using a noise-assisted method, and the process was accelerated by learning from the K-singular value decomposition strategy. Finally, by testing the vital sign signals collected from the finite differences time-domain simulation and experiments, the results obtained indicate that the proposed approach is effective for the extraction of low-amplitude heartbeat signals from the respiration signal, and that it significantly improves the accuracy of heart rate evaluation.INDEX TERMS Convolutional sparse coding, heart rate, finite-difference time-domain, respiration rate, ultrawideband radar, noise-assisted method.

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Heart ID: Human Identification Based on Radar Micro-Doppler Signatures of the Heart Using Deep Learning

Cited by 27 publications

References 18 publications

Dual-Biometric Human Identification Using Radar Deep Transfer Learning

Dual-Biometric Human Identification Using Radar Deep Transfer Learning

Radar-Based Non-Contact Continuous Identity Authentication

Noncontact Heart Rate Measurement Based on an Improved Convolutional Sparse Coding Method Using IR-UWB Radar

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