Channel State Information for Human Activity Recognition with Low Sampling Rates

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Abstract

In this paper, it is shown that lower transmission/sampling rates can be used in human activity recognition using channel state information (F1-scores > 85%) and that extremely high sampling rates are unnecessary once the system has been deployed. This is done by analysing the effects of interpolating different sampling rates on Wi-Fi dynamic channel state information for human activity recognition. While current research focuses on training and testing with homogeneous and very high sampling rates (> 100 Hz), this paper outlines some issues with higher sampling rates and explores the impact of training and testing with heterogeneous sampling rates in order to advance more towards joint communication and sensing, where one cannot be certain of the received data rate over time while not knowing the exact training set due to weight sharing in Federated Learning. This paper shows the effect of training and testing with heterogeneous sampling rates (including interpolated datasets) on convolutional neural networks in WiFi sensing.
Original languageEnglish
Title of host publicationUMUM 2023
Subtitle of host publication2nd Workshop on Ubiquitous and Multi-domain User Modeling
Pages614-620
Publication statusPublished - 2023
Event2nd Workshop on Ubiquitous and Multi-domain User Modeling, UMUM 2023 - Atlanta, United States
Duration: 13 Mar 202317 Mar 2023
Conference number: 2

Conference

Conference2nd Workshop on Ubiquitous and Multi-domain User Modeling, UMUM 2023
Abbreviated titleUMUM 2023
Country/TerritoryUnited States
CityAtlanta
Period13/03/2317/03/23

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