Special Issue "Radar Remote Sensing on Life Activities"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (10 June 2020).

Special Issue Editors

Dr. Zhengyu Peng
E-Mail Website
Guest Editor
Aptiv Corporation, 2152 E Lincoln Rd, Kokomo, IN 46902, USA
Interests: automotive radar; mm-wave radar; radio frequency and microwave systems; antenna array; beamforming
Special Issues and Collections in MDPI journals
Dr. Changzhi Li
E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Texas Tech University, Box 43102, Lubbock, TX 79409-3102, USA
Interests: radio frequency and microwave; wireless localization; non-contact motion sensing; healthcare monitoring; structural monitoring; biomedical radar
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Non-contact remote sensing of life activities, such as respiration, heartbeat, hand gestures, sleep, and walking based on radar sensors has attracted a lot of interest from both academia and industry in recent years. Using radar sensors, researchers have been exploring novel applications including indoor tracking, monitoring of vital signs, security surveillance, gesture recognition, and occupancy detection. Various radar sensors from bench-top systems to silicon on-chip integration have been widely reported. The operation frequency of these radar sensors ranges from a few MHz to sub-THz. Advanced algorithms such as machine learning and blind signal separation have also been adapted for radar-based life activity sensing. While the rapid advancements in radar remote sensing technologies have shown great promise in improving life quality, there still exist significant challenges to be solved.

We invite manuscripts for this forthcoming Special Issue in all aspects regarding radar remote sensing on life activities. Both reviews and original research articles on systems, hardware, or algorithms are welcome. Reviews should provide an up-to-date overview for the state-of-the-art technologies such as remote and accurate vital signs monitoring, life activity tracking, non-contact human-computer interface based on remote sensing of gesture commands, or any other radar based remote life activity sensing topics that have experienced significant advancements in the past decade. Original research papers should focus on a new approach or solve an important problem in radar-based life activity remote sensing. If you have ideas to discuss before submission, please feel free to contact us. We look forward to receiving your manuscript submitted to this Special Issue.

Dr. Zhengyu Peng
Dr. Changzhi Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • non-contact sensing
  • radar
  • vital signs
  • life activity tracking
  • biomedical applications
  • human-computer interface
  • security monitoring
  • microwave
  • radio frequency

Published Papers (11 papers)

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Research

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Article
Cardiopulmonary Activity Monitoring Using Millimeter Wave Radars
Remote Sens. 2020, 12(14), 2265; https://doi.org/10.3390/rs12142265 - 15 Jul 2020
Cited by 1 | Viewed by 1001
Abstract
Current cardiopulmonary activity monitoring is based on contact devices which cannot be used in extreme cases such as premature infants, burnt victims or rescue operations. In order to overcome these limitations, the use of radar technologies emerges as an alternative. This paper aims [...] Read more.
Current cardiopulmonary activity monitoring is based on contact devices which cannot be used in extreme cases such as premature infants, burnt victims or rescue operations. In order to overcome these limitations, the use of radar technologies emerges as an alternative. This paper aims to enhance the comprehension that non-contact technologies, in particular radar techniques, offer as a monitoring tool. For this purpose, a modified low cost commercial 122 GHz frequency-modulated continuous-wave (FMCW) radar is used to better fit the current application domain. The radar signals obtained are processed using a classic linear filtering algorithm aiming to separate the breathing from the heartbeat component while preserving signals integrity. In a standoff configuration and with different subject orientations, results show that the signal obtained with the radar can be used to extract not only the respiratory and heartbeat rates, but also the heart rate variability (HRV) sequence. Moreover, results evidence the coupling between breathing and heartbeat, also showing that the HRV sequence obtained can identify the respiratory sinus arrhythmia (RSA) effect. Finally, the radar is tested in a simultaneous multi-target scenario, demonstrating its monitoring capabilities in more complex situations. Nevertheless, there are some challenges left to use the system in a real-life monitoring environments, such as the removal of random body movements. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Article
Hierarchical Radar Data Analysis for Activity and Personnel Recognition
Remote Sens. 2020, 12(14), 2237; https://doi.org/10.3390/rs12142237 - 12 Jul 2020
Cited by 1 | Viewed by 830
Abstract
Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with [...] Read more.
Radar-based classification of human activities and gait have attracted significant attention with a large number of approaches proposed in terms of features and classification algorithms. A common approach in activity classification attempts to find the algorithm (features plus classifier) that can deal with multiple activities analysed in one study such as walking, sitting, drinking and crawling. However, using the same set of features for multiple activities can be suboptimal per activity and not take into account the diversity of kinematic movements that could be captured by diverse features. In this paper, we propose a hierarchical classification approach that uses a large variety of features including but not limited to energy features like entropy and energy curve, physical features like centroid and bandwidth, image-based features like skewness extracted from multiple radar data domains. Feature selection is used at each step of the hierarchical model to select the best set of features to discriminate the target activity from the others, showing improvements with respect to the more conventional approach of using a multiclass model. The proposed approach is validated on a large dataset with 1078 recorded samples of varying length from 5 s to 10 s of experimental data, yielding 95.4% accuracy to classify six activities. The approach is also validated on a personnel recognition task to identify individual subjects from their walking gait, yielding 83.7% accuracy for ten subjects and 68.2% for a significantly larger group of subjects, i.e., 60 people. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Article
Non-Contact Speech Recovery Technology Using a 24 GHz Portable Auditory Radar and Webcam
Remote Sens. 2020, 12(4), 653; https://doi.org/10.3390/rs12040653 - 17 Feb 2020
Viewed by 862
Abstract
Language has been one of the most effective ways of human communication and information exchange. To solve the problem of non-contact robust speech recognition, recovery, and surveillance, this paper presents a speech recovery technology based on a 24 GHz portable auditory radar and [...] Read more.
Language has been one of the most effective ways of human communication and information exchange. To solve the problem of non-contact robust speech recognition, recovery, and surveillance, this paper presents a speech recovery technology based on a 24 GHz portable auditory radar and webcam. The continuous-wave auditory radar is utilized to extract the vocal vibration signal, and the webcam is used to obtain the fitted formant frequency. The traditional formant speech synthesizer is selected to synthesize and recover speech, using the vocal vibration signal as the sound source excitation and the fitted formant frequency as the vocal tract resonance characteristics. Experiments on reading single English characters and words are carried out. Using microphone records as a reference, the effectiveness of the proposed speech recovery technology is verified. Mean opinion scores show a relatively high consistency between the synthesized speech and original acoustic speech. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Article
Arm Motion Classification Using Time-Series Analysis of the Spectrogram Frequency Envelopes
Remote Sens. 2020, 12(3), 454; https://doi.org/10.3390/rs12030454 - 01 Feb 2020
Cited by 6 | Viewed by 995
Abstract
Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man–machine interfaces and smart environments. In this paper, we use the time-series analysis method to accurately measure the similarity of the micro-Doppler (MD) signatures between the training and test [...] Read more.
Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man–machine interfaces and smart environments. In this paper, we use the time-series analysis method to accurately measure the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply two machine learning (ML) techniques, namely, the dynamic time warping (DTW) method and the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and characteristics of the time-series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Article
Blood Glucose Level Monitoring Using an FMCW Millimeter-Wave Radar Sensor
Remote Sens. 2020, 12(3), 385; https://doi.org/10.3390/rs12030385 - 25 Jan 2020
Cited by 10 | Viewed by 2132
Abstract
In this article, a novel sensing approach is presented for glucose level monitoring where a robust low-power millimeter(mm)-wave radar system is used to differentiate between blood samples of disparate glucose concentrations in the range 0.5 to 3.5 mg/mL. The proposed radar sensing mechanism [...] Read more.
In this article, a novel sensing approach is presented for glucose level monitoring where a robust low-power millimeter(mm)-wave radar system is used to differentiate between blood samples of disparate glucose concentrations in the range 0.5 to 3.5 mg/mL. The proposed radar sensing mechanism shows greater capabilities for remote detection of blood glucose inside test tubes through detecting minute changes in their dielectric properties. In particular, the reflected mm-waves that represent unique signatures for the internal synthesis and composition of the tested blood samples, are collected from the multi-channels of the radar and analyzed using signal processing techniques to identify different glucose concentrations and correlate them to the reflected mm-wave readings. The mm-wave spectrum is chosen for glucose sensing in this study after a set of preliminary experiments that investigated the dielectric permittivity behavior of glucose-loaded solutions across different frequency bands. In this regard, a newly-developed commercial coaxial probe kit (DAK-TL) is used to characterize the electromagnetic properties of glucose-loaded samples in a broad range of frequencies from 300 MHz to 67 GHz using two different 50 Ω open-coaxial probes. This would help to determine the portion of the frequency spectrum that is more sensitive to slight variations in glucose concentrations as indicated by the amount of change in the dielectric constant and loss tangent parameters due to the different concentrations under test. The mm-wave frequency range 50 to 67 GHz has shown to be promising for acquiring both high sensitivity and sufficient penetration depth for the most interaction between the glucose molecules and electromagnetic waves. The processed results have indicated the reliability of using mm-wave radars in identifying changes in blood glucose levels while monitoring trends among those variations. Particularly, blood samples of higher glucose concentrations are correlated with reflected mm-wave signals of greater energy. The proposed system could likely be adapted in the future as a portable non-invasive continuous blood glucose level monitoring for daily use by diabetics. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Article
A Mutiscale Residual Attention Network for Multitask Learning of Human Activity Using Radar Micro-Doppler Signatures
Remote Sens. 2019, 11(21), 2584; https://doi.org/10.3390/rs11212584 - 04 Nov 2019
Cited by 5 | Viewed by 1126
Abstract
Short-range radar has become one of the latest sensor technologies for the Internet of Things (IoT), and it plays an increasingly vital role in IoT applications. As the essential task for various smart-sensing applications, radar-based human activity recognition and person identification have received [...] Read more.
Short-range radar has become one of the latest sensor technologies for the Internet of Things (IoT), and it plays an increasingly vital role in IoT applications. As the essential task for various smart-sensing applications, radar-based human activity recognition and person identification have received more attention due to radar’s robustness to the environment and low power consumption. Activity recognition and person identification are generally treated as separate problems. However, designing different networks for these two tasks brings a high computational complexity and wastes of resources to some extent. Furthermore, there are some correlations in activity recognition and person identification tasks. In this work, we propose a multiscale residual attention network (MRA-Net) for joint activity recognition and person identification with radar micro-Doppler signatures. A fine-grained loss weight learning (FLWL) mechanism is presented for elaborating a multitask loss to optimize MRA-Net. In addition, we construct a new radar micro-Doppler dataset with dual labels of activity and identity. With the proposed model trained on this dataset, we demonstrate that our method achieves the state-of-the-art performance in both radar-based activity recognition and person identification tasks. The impact of the FLWL mechanism was further investigated, and ablation studies of the efficacy of each component in MRA-Net were also conducted. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Article
An Accurate Method to Distinguish Between Stationary Human and Dog Targets Under Through-Wall Condition Using UWB Radar
Remote Sens. 2019, 11(21), 2571; https://doi.org/10.3390/rs11212571 - 01 Nov 2019
Cited by 5 | Viewed by 1149
Abstract
Research work on distinguishing humans from animals can help provide priority orders and optimize the distribution of resources in earthquake- or mining-related rescue missions. However, the existing solutions are few and their stability and accuracy of classification are less. This study proposes an [...] Read more.
Research work on distinguishing humans from animals can help provide priority orders and optimize the distribution of resources in earthquake- or mining-related rescue missions. However, the existing solutions are few and their stability and accuracy of classification are less. This study proposes an accurate method for distinguishing stationary human targets from dog targets under through-wall condition based on ultra-wideband (UWB) radar. Eight humans and five beagles were used to collect 130 samples of through-wall signals using the UWB radar. Twelve corresponding features belonging to four categories were combined using the support vector machine (SVM) method. A recursive feature elimination (RFE) method determined an optimal feature subset from the twelve features to overcome overfitting and poor generalization. The results after ten-fold cross-validation showed that the area under the receiver operator characteristic (ROC) curve can reach 0.9993, which indicates that the two subjects can be distinguished under through-wall condition. The study also compared the ability of the proposed features of four categories when used independently in a classifier. Comparison results indicated that wavelet entropy-corresponding features among them have the best performance. The method and results are envisioned to be applied in various practical situations, such as post-disaster searching, hostage rescues, and intelligent homecare. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Article
Detection and Localization for Multiple Stationary Human Targets Based on Cross-Correlation of Dual-Station SFCW Radars
Remote Sens. 2019, 11(12), 1428; https://doi.org/10.3390/rs11121428 - 15 Jun 2019
Cited by 5 | Viewed by 1216
Abstract
This paper demonstrates the feasibility of detection and localization of multiple stationary human targets based on cross-correlation of the dual-station stepped-frequency continuous-wave (SFCW) radars. Firstly, a cross-correlation operation is performed on the preprocessed pulse signals of two SFCW radars at different locations to [...] Read more.
This paper demonstrates the feasibility of detection and localization of multiple stationary human targets based on cross-correlation of the dual-station stepped-frequency continuous-wave (SFCW) radars. Firstly, a cross-correlation operation is performed on the preprocessed pulse signals of two SFCW radars at different locations to obtain the correlation coefficient matrix. Then, the constant false alarm rate (CFAR) detection is applied to extract the ranges between each target and the two radars, respectively, from the correlation matrix. Finally, the locations of human targets is calculated with the triangulation localization algorithm. This cross-correlation operation mainly brings about two advantages. On the one hand, the cross-correlation explores the correlation feature of target respiratory signals, which can effectively detect all targets with different signal intensities, avoiding the missed detection of weak targets. On the other hand, the pairing of two ranges between each target and two radars is implemented simultaneously with the cross-correlation. Experimental results verify the effectiveness of this algorithm. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Article
A Novel Vital-Sign Sensing Algorithm for Multiple Subjects Based on 24-GHz FMCW Doppler Radar
Remote Sens. 2019, 11(10), 1237; https://doi.org/10.3390/rs11101237 - 24 May 2019
Cited by 31 | Viewed by 2385
Abstract
A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs [...] Read more.
A novel non-contact vital-sign sensing algorithm for use in cases of multiple subjects is proposed. The approach uses a 24 GHz frequency-modulated continuous-wave Doppler radar with the parametric spectral estimation method. Doppler processing and spectral estimation are concurrently implemented to detect vital signs from more than one subject, revealing excellent results. The parametric spectral estimation method is utilized to clearly identify multiple targets, making it possible to distinguish multiple targets located less than 40 cm apart, which is beyond the limit of the theoretical range resolution. Fourier transformation is used to extract phase information, and the result is combined with the spectral estimation result. To eliminate mutual interference, the range integration is performed when combining the range and phase information. By considering breathing and heartbeat periodicity, the proposed algorithm can accurately extract vital signs in real time by applying an auto-regressive algorithm. The capability of a contactless and unobtrusive vital sign measurement with a millimeter wave radar system has innumerable applications, such as remote patient monitoring, emergency surveillance, and personal health care. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Review

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Review
Radar-Based Non-Contact Continuous Identity Authentication
Remote Sens. 2020, 12(14), 2279; https://doi.org/10.3390/rs12142279 - 15 Jul 2020
Cited by 6 | Viewed by 1373
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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Review
A Survey of Deep Learning-Based Human Activity Recognition in Radar
Remote Sens. 2019, 11(9), 1068; https://doi.org/10.3390/rs11091068 - 06 May 2019
Cited by 34 | Viewed by 3411
Abstract
Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely [...] Read more.
Radar, as one of the sensors for human activity recognition (HAR), has unique characteristics such as privacy protection and contactless sensing. Radar-based HAR has been applied in many fields such as human–computer interaction, smart surveillance and health assessment. Conventional machine learning approaches rely on heuristic hand-crafted feature extraction, and their generalization capability is limited. Additionally, extracting features manually is time–consuming and inefficient. Deep learning acts as a hierarchical approach to learn high-level features automatically and has achieved superior performance for HAR. This paper surveys deep learning based HAR in radar from three aspects: deep learning techniques, radar systems, and deep learning for radar-based HAR. Especially, we elaborate deep learning approaches designed for activity recognition in radar according to the dimension of radar returns (i.e., 1D, 2D and 3D echoes). Due to the difference of echo forms, corresponding deep learning approaches are different to fully exploit motion information. Experimental results have demonstrated the feasibility of applying deep learning for radar-based HAR in 1D, 2D and 3D echoes. Finally, we address some current research considerations and future opportunities. Full article
(This article belongs to the Special Issue Radar Remote Sensing on Life Activities)
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