Sensing Direction of Human Motion Using Single-Input-Single-Output (SISO) Channel Model and Neural Networks

Bhat, Sameer Ahmad; Dar, Muneer Ahmad; Szczuko, Piotr; Alyahya, Dalia; Mustafa, Farhana

doi:10.1109/access.2022.3177273

Cited by 3 publications

(1 citation statement)

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“…The trained model is then used to accurately classify new datasets into the respective movement classes [16]. Relevant studies have used methods that include not only artificial neural networks (ANN) and support vector machines [17], but the more advanced deep learning techniques such as convolutional neural network and long short-term memory neural networks [18]. Even with the advantages offered by deep learning, the methods are computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Human Movement Classification Using Wi-Fi Based Passive Forward Scattering Radar

Farhan Nasarudin,

Razali,

Ismail

et al. 2023

TEM Journal

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Forward scattering radar has been widely used to classify human movements. To reduce concern of prolonged radiation exposure, Wi-Fi based passive scattering radar with bistatic topology is proposed. The movements include walking, bending, sitting, and kneeling. The enveloping algorithm applied to Wi-Fi based passive forward scattering radar signals has produced a comparable pattern to the ones from the active configuration. The features extracted from the frequency spectrum are also similar for both radar topologies. These led to comparable performance when classifying using artificial neural network. Hence, Wi-Fi can effectively reduce the cost of radar sensors.

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Section: Introductionmentioning

confidence: 99%