Robustness analysis of Wi-Fi-based human activity recognition

Ajaya Dahal; Sabyasachi Biswas; Sevgi Z. Gurbuz; Ali C. Gurbuz

doi:10.1117/12.3014010

10 June 2024 Robustness analysis of Wi-Fi-based human activity recognition

Ajaya Dahal, Sabyasachi Biswas, Sevgi Z. Gurbuz, Ali C. Gurbuz

Proceedings Volume 13036, Big Data VI: Learning, Analytics, and Applications; 130360J (2024) https://doi.org/10.1117/12.3014010
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Researchers are exploring how radio frequency (RF) sensors can be used to create new interfaces and smart environments that respond to human movement. This technology has the potential to be used in things like gesture recognition or smart home systems. While there are different types of RF sensors that can be used, this study focuses on using Wi-Fi signals for this purpose. The researchers collected data using a Raspberry Pi equipped with special software. They then analyzed this data to see if it could be used to identify different human activities. They made their data and code publicly available so that others can build on their work. The study found that Wi-Fi signals could be used to identify activities with an accuracy of around 65%. This suggests that Wi-Fi has potential for being used to monitor activity indoors.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ajaya Dahal, Sabyasachi Biswas, Sevgi Z. Gurbuz, and Ali C. Gurbuz "Robustness analysis of Wi-Fi-based human activity recognition", Proc. SPIE 13036, Big Data VI: Learning, Analytics, and Applications, 130360J (10 June 2024); https://doi.org/10.1117/12.3014010

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