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A 77 GHz frequency-modulated continuous wave (FMCW) radar was utilized to extract biomechanical parameters for gait analysis in indoor scenarios. By preprocessing the collected raw radar data and eliminating environmental noise, a range–velocity–time (RVT) data cube encompassing the subjects' information was derived. The strongest signals from the torso in the velocity and range dimensions and the enveloped signal from the toe in the velocity dimension were individually separated for the gait parameters extraction. Then, six gait parameters, including step time, stride time, step length, stride length, torso velocity, and toe velocity, were measured. In addition, the Qualisys system was concurrently utilized to measure the gait parameters of the subjects as the ground truth. The reliability of the parameters extracted by the radar was validated through the application of the Wilcoxon test, the intraclass correlation coefficient (ICC) value, and Bland–Altman plots. The average errors of the gait parameters in the time, range, and velocity dimensions were less than 0.004 s, 0.002 m, and 0.045 m/s, respectively. This non-contact radar modality promises to be employable for gait monitoring and analysis of the elderly at home. [ABSTRACT FROM AUTHOR]

Using mm-wave sensing to identify persons by the way they walk has recently emerged as a promising solution of biometrics, and has found versatile applications in security surveillance, automatic access control, and health monitoring. In this paper, we build a system named MGait for indoor multi-person identification and intruder detection based on gait micro-Doppler (m-D) signatures captured by a low-cost mm-wave radar. In multi-person scenario where multiple subjects concurrently share and walk within the same physical space, the proposed system continuously detects and separately tracks each subject in the range-Doppler (R-D) space frame by frame to extract their respective gait m-D signatures. Then, an open-set identification network is trained by a large-margin Gaussian mixture (L-GM) loss to learn highly discriminative feature representations and compel the learned features of the training set to follow a Gaussian mixture distribution, with each component representing a registered user. As such, the known users can be directly identified based on the class-posterior probability and the intruder can also be rejected by setting a probability threshold. The proposed system is verified on real radar measurements collected by a 77 GHz FMCW radar, achieving an overall accuracy of 88.59% in identifying up to 5 subjects, including 1 known user and 4 intruders, freely and concurrently moving in a corridor space. [ABSTRACT FROM AUTHOR]

Building a robust and scalable radar-based person identification system that can accurately identify registered users and fast enroll new subjects at any time with only limited radar measurements is very desirable for real-world applications. Existing solutions, however, most resort to deep learning techniques that are heavily data-dependent and hard to scale once trained. To overcome these limitations, this article proposes a novel set-based discriminative subspaces learning (SDSL) approach and builds a high-performance gait recognition system based on it. Specifically, we model the system as a two-stage paradigm consisting of a feature embedding network for gait feature extraction, followed by an SDSL module for gait pattern modeling through low-dimensional linear subspace representing and subspaces discriminant learning. Finally, a nearest neighbor classifier (NNC) is adopted to perform person identification in the learned discriminant space by matching the subspace of a probe set with all subspaces in the gallery. Extensive experiments conducted on a radar gait dataset collected in a typical corridor scenario demonstrate not only the superiority of our model in both identification accuracy and scalability, but also its robustness against limited training data. [ABSTRACT FROM AUTHOR]