Indoor Person Identification Using a Low-Power FMCW Radar

Vandersmissen, Baptist; Knudde, Nicolas; Jalalvand, Azarakhsh; Couckuyt, Ivo; Bourdoux, André; Neve, Wesley De; Dhaene, Tom

doi:10.1109/tgrs.2018.2816812

Cited by 194 publications

(131 citation statements)

References 14 publications

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“…Given f 0 , we proceed to calculate the gait harmonic frequency ratio β using (11). Table I shows the classification results using solely the β feature for all considered walking styles.…”

Section: B Physical Features Based On Sum-of-harmonics Analysismentioning

confidence: 99%

Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler Signatures

Seifert

Amin

Zoubir

2019

IEEE Trans. Biomed. Eng.

112

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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 radarbased 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 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.

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“…Given f 0 , we proceed to calculate the gait harmonic frequency ratio β using (11). Table I shows the classification results using solely the β feature for all considered walking styles.…”

Section: B Physical Features Based On Sum-of-harmonics Analysismentioning

confidence: 99%

Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler Signatures

Seifert

Amin

Zoubir

2019

IEEE Trans. Biomed. Eng.

112

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“…An FMCW radar produced by INRAS is used in SISO mode for creating the IDRad dataset [3]. A micro-Doppler map is calculated by first determining a range-Doppler map using a two-dimensional Fourier transform and by subsequently summing the absolute values over the range dimension.…”

Section: A Pre-processing the Input Featuresmentioning

confidence: 99%

“…For example, to classify human actions, Kim et al [2] have presented an approach using manual feature extraction and support vector machines (SVM). Furthermore, given the spectrogram shape of MD signatures, it is not surprising to observe that several convolutional neural network-based approaches have also been introduced for different use cases [3], [4], [5]. Also, in [6], different human actions have been modeled and classified by hidden Markov models (HMMs) in combination with principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the authors report an accuracy of 92.4% on gender classification. Moreover, in [3], a deep convolutional neural network (DCNN) is designed for identifying five persons. Using original and pre-processed radar data, an accuracy is obtained of 53.17% and 73.35%, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The aim of our work is to setup and employ RCNs for identifying humans based on their gait in an indoor environment, using MD signatures provided by a low-power FMCW radar. All of our training and validation experiments have been conducted on IDRad [3], a publicly available radar data set that consists of five persons walking spontaneously and individually in an indoor environment.…”

Section: Introductionmentioning

confidence: 99%

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Radar Signal Processing for Human Identification by Means of Reservoir Computing Networks

Jalalvand

Vandersmissen

Neve

et al. 2019

2019 IEEE Radar Conference (RadarConf)

Self Cite

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Along with substantial advances in the area of image processing and, consequently, video-based surveillance systems, concerns about preserving the privacy of people have also deepened. Therefore, replacing conventional video cameras in surveillance systems with less-intrusive and yet effective alternatives, such as micro-wave radars, is of high interest. The aim of this work is to explore the application of Reservoir Computing Networks (RCNs) to the problem of identifying a limited number of people in an indoor environment, leveraging gait information captured by micro-wave radar measurements. These measurements are done using a commercial low-power linear frequency-modulated continuous-wave (FMCW) radar.Besides the low quality of the outputs of such a radar sensor, walking spontaneously as opposed to controlled situations adds another level of complexity to the targeted use case. In this context, RCNs are interesting tools, given that they have shown a high effectiveness in capturing temporal information and handling noise, while at the same time being easy to setup and train. Using Micro-Doppler features as inputs, we follow a structured procedure towards optimizing the parameters of our RCN-based approach, showing that RCNs have a great potential in processing the noisy features provided by a low-power radar.

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Person identification with limited training data using radar micro‐Doppler signatures

Lang

Wang

Yang

et al. 2019

Micro & Optical Tech Letters

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Radar‐based human micro‐Doppler analysis has been the subject of much investigation in recent years. Apart from the conventional activity classification task, person identification based on human movement signal has emerged as a research interest. This paper presents a method to recognize a person's identity from varied human motions using an ultra‐wideband radar. The human movement data is captured in an indoor environment and is then transformed into micro‐Doppler spectrograms for identification. Moreover, as it is always challenging to construct large scale radar datasets in practice, we adopt a plain convolutional neural network with a multi‐scale feature aggregation strategy to address the identification problem. Experimental results show that the micro‐Doppler signatures have great potential in person identification, and our model presents relative satisfying performances limited training set. Especially, when “walking” is used for identification, our approach achieves a person identification accuracy of 96.8% for the four targets used.

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Indoor Person Identification Using a Low-Power FMCW Radar

Cited by 194 publications

References 14 publications

Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler Signatures

Toward Unobtrusive In-Home Gait Analysis Based on Radar Micro-Doppler Signatures

Radar Signal Processing for Human Identification by Means of Reservoir Computing Networks

Person identification with limited training data using radar micro‐Doppler signatures

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