Multiple joint-variable domains recognition of human motion

Jokanović, Branka; Amin, Moeness G.; Erol, Barış

doi:10.1109/radar.2017.7944340

Cited by 43 publications

(29 citation statements)

References 20 publications

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“…and utilizing the nonlinear least squares (NLS) method for estimating f 0 , which is augmented by a model order selection method for detecting q. For this, (14) is jointly optimized over candidate FFs and candidate orders. We use f mD as an initial estimate of the FF.…”

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.

122

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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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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.

122

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“…To sum up, the output total loss function in the training stage to optimize the DopNet is shown in Eqn. (9). Once the network parameters are stored and saved, in the test stage, given a test sample x test , the predicted label can be calculated by the simple max operation of the Logit sof t in Eqn.…”

Section: Single-channel(sc)-dopnetmentioning

confidence: 99%

“…In addition, µ-DS have been used to distinguish wind turbine blades and the blades of aircraft rotors [7]. It has also been demonstrated how µ-DS of different human target movements can help increase the situational awareness of the ambient assistant living in the healthcare context [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

DopNet: A Deep Convolutional Neural Network to Recognize Armed and Unarmed Human Targets

et al. 2019

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The work presented in this paper aims to distinguish between armed or unarmed personnel using multi-static radar data and advanced Doppler processing. We propose two modified Deep Convolutional Neural Networks (DCNN) termed SC-DopNet and MC-DopNet for mono-static and multi-static micro-Doppler signature (µ-DS) classification. Differentiating armed and unarmed walking personnel is challenging due to the effect of aspect angle and channel diversity in real-world scenarios. In addition, DCNN easily overfits the relatively small-scale µ-DS dataset. To address these problems, the work carried out in this paper makes three key contributions: first, two effective schemes including data augmentation operation and a regularization term are proposed to train SC-DopNet from scratch. Next, a factor analysis of the SC-DopNet are conducted based on various operating parameters in both the processing and radar operations. Thirdly, to solve the problem of aspect angle diversity for µ-DS classification, we design MC-DopNet for multi-static µ-DS which is embedded with two new fusion schemes termed as Greedy Importance Reweighting (GIR) and 21-Norm. These two schemes are based on two different strategies and have been evaluated experimentally: GIR uses a "win by sacrificing worst case" whilst 21-Norm adopts a "win by sacrificing best case" approach. The SC-DopNet outperforms the non-deep methods by 12.5% in average and the proposed MC-DopNet with two fusion methods outperforms the conventional binary voting by 1.2% in average. Note that we also argue and discuss how to utilize the statistics of SC-DopNet results to infer the selection of fusion strategies for MC-DopNet under different experimental scenarios.

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“…In addition, µ-DS have been used to distinguish wind turbine blades and the blades of aircraft rotors [6]. It has also been demonstrated how µ-DS of different human target movements can help increase the situational awareness of the ambient assistant living in the healthcare context [7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, MMD-DAN is optimized in Eq. (7) while JS-DAN optimization function is shown in Eq (8)…”

mentioning

confidence: 99%

Eliminate Aspect Angle Variations for Human Activity Recognition using Unsupervised Deep Adaptation Network

Chen

Liu

Fioranelli

et al. 2019

2019 IEEE Radar Conference (RadarConf)

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Activity recognition and monitoring using radar micro-Doppler signatures (µ-DS) classification has played an vital part in various security and healthcare applications. In the practical scenario, aspect angle variations of µ-DS increase the data diversity but can be regarded as a distraction factor for activity recognition. The learned feature extractor and classifier will degrade a lot if the test µ-DS is from a different aspect angle from the training dataset. This is because the aspect angle variations between training and test dataset will break the assumption of the classification methods: the training and test data are drawn from the same distribution. This paper aims to eliminate the aspect angle variations by learning aspect angle invariant and meanwhile discriminative features in the bi-static radar geometry using the unlabeled test data. More specifically, we first propose a new problem to train a feature extractor using certain aspect angles but generalizes well for other aspect angles in the test stage. Next, we propose two adaptation networks termed as MMD-DAN and JS-DAN, utilizing two widely used distribution divergence measurements. Finally, we evaluate our experimental setting and methods using experimental data.

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Multiple joint-variable domains recognition of human motion

Cited by 43 publications

References 20 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

DopNet: A Deep Convolutional Neural Network to Recognize Armed and Unarmed Human Targets

Eliminate Aspect Angle Variations for Human Activity Recognition using Unsupervised Deep Adaptation Network

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