Journal ArticleDOI
RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Network
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TLDR
HAR-SAnet is a novel RF-based HAR framework that adopts an original signal adapted convolutional neural network architecture that substantially outperforms the state-of-the-art algorithms and systems.Abstract:
Human Activity Recognition (HAR) plays a critical role in a wide range of real-world applications, and it is traditionally achieved via wearable sensing. Recently, to avoid the burden and discomfort caused by wearable devices, device-free approaches exploiting RF signals arise as a promising alternative for HAR. Most of the latest device-free approaches require training a large deep neural network model in either time or frequency domain, entailing extensive storage to contain the model and intensive computations to infer activities. Consequently, even with some major advances on device-free HAR, current device-free approaches are still far from practical in real-world scenarios where the computation and storage resources possessed by, for example, edge devices, are limited. Therefore, we introduce HAR-SAnet which is a novel RF-based HAR framework. It adopts an original signal adapted convolutional neural network architecture: instead of feeding the handcraft features of RF signals into a classifier, HAR-SAnet fuses them adaptively from both time and frequency domains to design an end-to-end neural network model. We apply point-wise grouped convolution and depth-wise separable convolutions to confine the model scale and to speed up the inference execution time. The experiment results show that the recognition accuracy of HAR-SAnet outperforms state-of-the-art algorithms and systems.read more
Citations
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Octopus: a practical and versatile wideband MIMO sensing platform
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TL;DR: Octopus as mentioned in this paper is a programmable wideband MIMO sensing platform, built using commercial-grade low-power impulse radio, which allows for quick algorithm design and application development, and specifically leverages the wideband radio to achieve a competent and robust performance in practice.
Journal ArticleDOI
Deep learning and transfer learning for device-free human activity recognition: A survey
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CORE-lens: simultaneous communication and object recognition with disentangled-GAN cameras
Ziwei Liu,Tianyue Zheng,Chao Hu,Yanbing Yang,Yimao Sun,Yi Zhang,Zhe Chen,Liangyin Chen,Jun Luo +8 more
TL;DR: CORE-Lens exploits the idea of disentangled representation learning to separate the mixed signals in the feature space: while the GAN-reconstructed clean background images are used to perform object recognition, OCC decoding is conducted on the residual of the original image after subtracting the reconstructed background.
Journal ArticleDOI
Acoustic- and Radio-Frequency-Based Human Activity Recognition
TL;DR: This work used a hybrid approach, employing RF and acoustic signals to recognize falling, walking, sitting on a chair, and standing up from a chair to demonstrate the advantage of combining two non-invasive sensors in Human Activity Recognition (HAR) systems and smart assisted living.
Smart User Authentication through Actuation of Daily Activities Leveraging WiFi-enabled IoT
TL;DR: In this article, a deep learning-based user authentication scheme was proposed to accurately identify each individual user in both walking and stationary activities using the channel state information (CSI) measurements of WiFi signals.
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