Human–human interaction recognition based on ultra-wideband radar

Li, Haiping; Yang, Ruixia; Yang, Yang; Hou, Chunping; Hu, Zhiqi; Jiang, Tianli

doi:10.1007/s11760-020-01658-8

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…To select a learning framework to evaluate the recognition performance of the proposed method, comparative experiments were performed on five different learning models including PCA based SVM, because PCA based SVM method is widely used in the motion classification. In this paper, the learning frameworks considered are GoogleNet, ResNet, VGG, AlexNet, and PCA based SVM [ 34 , 39 , 40 , 51 , 52 , 53 ]. Comparative recognition performance is summarized in Table 2 .…”

Section: Recognition Resultsmentioning

confidence: 99%

“…On the other hand, deep learning approaches based on multi-layer networks such as the convolutional neural network (CNN) are promising for overcoming such feature selection problems without advance need for feature sets. GoogleNet, AlexNet, VGGNet, ResNet, and DenseNet are good examples of deep learning models [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

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Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Song

Kim

et al. 2021

Sensors

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Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

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