Radar-Based Human Gait Recognition Using Dual-Channel Deep Convolutional Neural Network

Bai, Xueru; Ye, Hui; Wang, Li; Zhou, Feng

doi:10.1109/tgrs.2019.2929096

Cited by 71 publications

(20 citation statements)

References 28 publications

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“…2 illustrates the spectrograms acquired from six different walking gaits. Positive and negative Doppler frequencies [33], [34] are caused by reversal in net direction (towards/away) with respect to the radar. It may be observed that some pairs of gaits (e.g.…”

Section: A Feature Fusion With Conventional Classifiersmentioning

confidence: 99%

Sequential Human Gait Classification With Distributed Radar Sensor Fusion

Mehul

Kernec

et al. 2021

IEEE Sensors J.

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Section: A Feature Fusion With Conventional Classifiersmentioning

confidence: 99%

Sequential Human Gait Classification With Distributed Radar Sensor Fusion

Mehul

Kernec

et al. 2021

IEEE Sensors J.

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“…The resulting fake native signatures were then used to pretrain a three-layer convolutional autoencoder (CAE), which has been shown to surpass transfer learning in efficacy on small RF datasets [18], [19]. In each layer, a filter concatenation technique [20] is employed in which a filter size of 3 × 3 and 9 × 9 were concatenated to take advantage of multilevel feature extraction. After training the CAE model, the decoder was removed and two fully connected layers with 128 neurons followed by a dropout of 0.55 were added after flattening the output of the encoder.…”

Section: A Transformation Of Imitation To "Fake Native" Datamentioning

confidence: 99%

Word-Level ASL Recognition and Trigger Sign Detection with RF Sensors

Rahman

Kurtoğlu

Mdrafi

et al. 2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Current research in the recognition of American Sign Language (ASL) has focused on perception using video or wearable gloves. However, deaf ASL users have expressed concern about the invasion of privacy with video, as well as the interference with daily activity and restrictions on movement presented by wearable gloves. In contrast, RF sensors can mitigate these issues as it is a non-contact ambient sensor that is effective in the dark and can penetrate clothes, while only recording speed and distance. Thus, this paper investigates RF sensing as an alternative sensing modality for ASL recognition to facilitate interactive devices and smart environments for the deaf and hardof-hearing. In particular, the recognition of up to 20 ASL signs, sequential classification of signing mixed with daily activity, and detection of a trigger sign to initiate human-computer interaction (HCI) via RF sensors is presented. Results yield %91.3 ASL word-level classification accuracy, %92.3 sequential recognition accuracy, 0.93 trigger recognition rate.

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“…Comparison with conventional supervisedlearning classifiers such as Naïve Bayes and SVM were provided, demonstrating better performances when using the DCNN. A DCNN was also used in [14] for human gait recognition, exploiting a dual-channel architecture where the network had two separate branches at the input, in order to accept spectrograms calculated with different temporal resolutions. A specifically designed DCNN was also used in [11] to identify specific individuals in different rooms based on their walking gait, with the additional complexity of the subjects following free-form, unconstrained trajectories.…”

Section: Introductionmentioning

confidence: 99%

Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks

et al. 2020

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Recognition of human movements with radar for ambient activity monitoring is a developed area of research that yet presents outstanding challenges to address. In real environments, activities and movements are performed with seamless motion, with continuous transitions between activities of different duration and a large range of dynamic motions, compared with discrete activities of fixed-time lengths which are typically analysed in the literature. This paper proposes a novel approach based on recurrent LSTM and Bi-LSTM network architectures for continuous activity monitoring and classification. This approach uses radar data in the form of a continuous temporal sequence of micro-Doppler or range-time information, differently from from other conventional approaches based on convolutional networks that interpret the radar data as images. Experimental radar data involving 15 participants and different sequences of 6 actions are used to validate the proposed approach. It is demonstrated that using the Dopplerdomain data together with the Bi-LSTM network and an optimal learning rate can achieve over 90% mean accuracy, whereas range-domain data only achieved approximately 76%. The details of the network architectures, insights in their behaviour as a function of key hyper-parameters such as the learning rate, and a discussion on their performance across are provided in the paper.

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Radar-Based Human Gait Recognition Using Dual-Channel Deep Convolutional Neural Network

Cited by 71 publications

References 28 publications

Sequential Human Gait Classification With Distributed Radar Sensor Fusion

Sequential Human Gait Classification With Distributed Radar Sensor Fusion

Word-Level ASL Recognition and Trigger Sign Detection with RF Sensors

Continuous Human Activity Classification From FMCW Radar With Bi-LSTM Networks

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