80 research outputs found
Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers
Obtaining a smart surveillance requires a sensing system that can capture
accurate and detailed information for the human walking style. The radar
micro-Doppler (-D) analysis is proved to be a reliable metric
for studying human locomotions. Thus, -D signatures can be
used to identify humans based on their walking styles. Additionally, the
signatures contain information about the radar cross section (RCS) of the
moving subject. This paper investigates the effect of human body
characteristics on human identification based on their -D
signatures. In our proposed experimental setup, a treadmill is used to collect
-D signatures of 22 subjects with different genders and body
characteristics. Convolutional autoencoders (CAE) are then used to extract the
latent space representation from the -D signatures. It is
then interpreted in two dimensions using t-distributed stochastic neighbor
embedding (t-SNE). Our study shows that the body mass index (BMI) has a
correlation with the -D signature of the walking subject. A
50-layer deep residual network is then trained to identify the walking subject
based on the -D signature. We achieve an accuracy of 98% on
the test set with high signal-to-noise-ratio (SNR) and 84% in case of different
SNR levels.Comment: Accepted in IEEE Radarconf1
Unsupervised Doppler Radar Based Activity Recognition for e-Healthcare
Passive radio frequency (RF) sensing and monitoring of human daily activities
in elderly care homes is an emerging topic. Micro-Doppler radars are an
appealing solution considering their non-intrusiveness, deep penetration, and
high-distance range. Unsupervised activity recognition using Doppler radar data
has not received attention, in spite of its importance in case of unlabelled or
poorly labelled activities in real scenarios. This study proposes two
unsupervised feature extraction methods for the purpose of human activity
monitoring using Doppler-streams. These include a local Discrete Cosine
Transform (DCT)-based feature extraction method and a local entropy-based
feature extraction method. In addition, a novel application of Convolutional
Variational Autoencoder (CVAE) feature extraction is employed for the first
time for Doppler radar data. The three feature extraction architectures are
compared with the previously used Convolutional Autoencoder (CAE) and linear
feature extraction based on Principal Component Analysis (PCA) and 2DPCA.
Unsupervised clustering is performed using K-Means and K-Medoids. The results
show the superiority of DCT-based method, entropy-based method, and CVAE
features compared to CAE, PCA, and 2DPCA, with more than 5\%-20\% average
accuracy. In regards to computation time, the two proposed methods are
noticeably much faster than the existing CVAE. Furthermore, for
high-dimensional data visualisation, three manifold learning techniques are
considered. The methods are compared for the projection of raw data as well as
the encoded CVAE features. All three methods show an improved visualisation
ability when applied to the encoded CVAE features
Gait recognition using FMCW radar and temporal convolutional deep neural netowrks
The capability of human identification in specific scenarios and in a quickly and accurately manner, is a critical aspect in various surveillance applications. In particular, in this context, classical surveillance systems are based on video cameras, requiring high computational/storing resources, which are very sensitive to light and weather conditions. In this paper, an efficient classifier based on deep learning is used for the purpose of identifying individuals features by resorting to the micro-Doppler data extracted from low-power frequency-modulated continuous-wave radar measurements. Results obtained through the application of a deep temporal convolutional neural networks confirms the applicability of deep learning to the problem at hand. Best obtained identification accuracy is 0.949 with an F-measure of 0.88 using a temporal window of four second
Radar for Assisted Living in the Context of Internet of Things for Health and Beyond
This paper discusses the place of radar for assisted living in the context of IoT for Health and beyond. First, the context of assisted living and the urgency to address the problem is described. The second part gives a literature review of existing sensing modalities for assisted living and explains why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that helps improve performances in classification especially with deep learning with a reflection on lessons learned from it. The fourth section introduces recent published work from our research group in the area that shows promise with multimodal sensor fusion for classification and long short-term memory applied to early stages in the radar signal processing chain. Finally, we conclude with open challenges still to be addressed in the area and open to future research directions in animal welfare
Sensor Fusion for Identification of Freezing of Gait Episodes Using Wi-Fi and Radar Imaging
Parkinson’s disease (PD) is a progressive and neurodegenerative condition causing motor impairments. One of the major motor related impairments that present biggest challenge is freezing of gait (FOG) in Parkinson’s patients. In FOG episode, the patient is unable to initiate, control or sustain a gait that consequently affects the Activities of Daily Livings (ADLs) and increases the occurrence of critical events such as falls. This paper presents continuous monitoring ADLs and classification freezing of gait episodes using Wi-Fi and radar imaging. The idea is to exploit the multi-resolution scalograms generated by channel state information (CSI) imprint and micro-Doppler signatures produced by reflected radar signal. A total of 120 volunteers took part in experimental campaign and were asked to perform different activities including walking fast, walking slow, voluntary stop, sitting down & stand up and freezing of gait. Two neural networks namely Autoencoder and a proposed enhanced Autoencoder were used classify ADLs and FOG episodes using data fusion process by combining the images acquired from both sensing techniques. The Autoencoder provided overall classification accuracy of ~87% for combined datasets. The proposed algorithm provided significantly better results by presenting an overall accuracy of ~98% using data fusion
Toward Deep Learning-Based Human Target Analysis
In this chapter, we describe methods toward deep learning-based human target analysis. Firstly, human target analysis in 2D and 3D domains of radar signal is introduced. Furthermore, range-Doppler surface for human target analysis using ultra-wideband radar is described. The construction of range-Doppler surface involves range-Doppler imaging, adaptive threshold detection, and isosurface extraction. In comparison with micro-Doppler profiles and high-resolution range profiles, range-Doppler surface contains range, Doppler, and time information simultaneously. An ellipsoid-based human motion model is designed for validation. Range-Doppler surfaces simulated for different human activities are demonstrated and discussed. With the rapid emergence of deep learning, the development of radar target recognition has been accelerated. We describe several deep learning algorithms for human target analysis. Finally, a few future research considerations are listed to spark inspiration
Toward Unobtrusive In-home Gait Analysis Based on Radar Micro-Doppler Signatures
Objective: In this paper, we demonstrate the applicability of radar for gait
classification with application to home security, medical diagnosis,
rehabilitation and assisted living. Aiming at identifying changes in gait
patterns based on radar micro-Doppler signatures, this work is concerned with
solving the intra motion category classification problem of gait recognition.
Methods: New gait classification approaches utilizing physical features,
subspace features and sum-of-harmonics modeling are presented and their
performances are evaluated using experimental K-band radar data of four test
subjects. Five different gait classes are considered for each person, including
normal, pathological and assisted walks. Results: The proposed approaches are
shown to outperform existing methods for radar-based gait recognition which
utilize physical features from the cadence-velocity data representation domain
as in this paper. The analyzed gait classes are correctly identified with an
average accuracy of 93.8%, where a classification rate of 98.5% is achieved for
a single gait class. When applied to new data of another individual a
classification accuracy on the order of 80% can be expected. Conclusion: Radar
micro-Doppler signatures and their Fourier transforms are well suited to
capture changes in gait. Five different walking styles are recognized with high
accuracy. Significance: Radar-based sensing of human gait is an emerging
technology with multi-faceted applications in security and health care
industries. We show that radar, as a contact-less sensing technology, can
supplement existing gait diagnostic tools with respect to long-term monitoring
and reproducibility of the examinations.Comment: 11 pages, 6 figure
Detection of Gait Asymmetry Using Indoor Doppler Radar
Doppler radar systems enable unobtrusive and privacy-preserving long-term
monitoring of human motions indoors. In particular, a person's gait can provide
important information about their state of health. Utilizing micro-Doppler
signatures, we show that radar is capable of detecting small differences
between the step motions of the two legs, which results in asymmetric gait.
Image-based and physical features are extracted from the radar return signals
of several individuals, including four persons with different diagnosed gait
disorders. It is shown that gait asymmetry is correctly detected with high
probability, irrespective of the underlying pathology, for at least one motion
direction.Comment: 6 pages, 5 figures, 4 tables; accepted at the IEEE Radar Conference
2019, Boston, MA, US
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