Despite recent advances, fast and reliable Human Activity Recognition in confined space is still an open problem related to many real-world applications, especially in health and biomedical monitoring. With the ubiquitous presence of Wi-Fi networks, the activity recognition and classification problems can be solved by leveraging some characteristics of the Channel State Information of the 802.11 standard. Given the well-documented advantages of Deep Learning algorithms in solving complex pattern recognition problems, many solutions in Human Activity Recognition domain are taking advantage of those models. To improve the time and precision of activity classification of time-series data stemming from Channel State Information, we propose herein a fast deep neural model encompassing concepts not only from state-of-the-art recurrent neural networks, but also using convolutional operators with added randomization. Results from real data in an experimental environment show promising results.

A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition / Succetti, F.; Rosato, A.; Di Luzio, F.; Ceschini, A.; Panella, M.. - In: ELECTROMAGNETIC WAVES. - ISSN 1070-4698. - 174:(2022), pp. 127-141. [10.2528/PIER22042605]

A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition

Succetti F.;Rosato A.;Di Luzio F.;Ceschini A.;Panella M.
2022

Abstract

Despite recent advances, fast and reliable Human Activity Recognition in confined space is still an open problem related to many real-world applications, especially in health and biomedical monitoring. With the ubiquitous presence of Wi-Fi networks, the activity recognition and classification problems can be solved by leveraging some characteristics of the Channel State Information of the 802.11 standard. Given the well-documented advantages of Deep Learning algorithms in solving complex pattern recognition problems, many solutions in Human Activity Recognition domain are taking advantage of those models. To improve the time and precision of activity classification of time-series data stemming from Channel State Information, we propose herein a fast deep neural model encompassing concepts not only from state-of-the-art recurrent neural networks, but also using convolutional operators with added randomization. Results from real data in an experimental environment show promising results.
2022
deep learning; randomization; Wi-Fi; human activity recognition
01 Pubblicazione su rivista::01a Articolo in rivista
A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition / Succetti, F.; Rosato, A.; Di Luzio, F.; Ceschini, A.; Panella, M.. - In: ELECTROMAGNETIC WAVES. - ISSN 1070-4698. - 174:(2022), pp. 127-141. [10.2528/PIER22042605]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1657000
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