SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing

Jianfei Yang, Xinyan Chen, Han Zou, Chris Xiaoxuan Lu, Dazhuo Wang, Sumei Sun, Lihua Xie

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving, ubiquitous human-sensing applications, enabled by signal processing and deep-learning methods. However, a comprehensive public benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does not yet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensing algorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluate various deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, and feature transferability. Extensive experiments are performed whose results provide valuable insights into model design, learning strategy, and training techniques for real-world applications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deep learning in WiFi sensing research that offers researchers a convenient tool to validate learning-based WiFi-sensing methods on multiple datasets and platforms.

Original languageEnglish
Article number100703
Pages (from-to)1-22
Number of pages23
JournalPatterns
Volume4
Issue number3
Early online date28 Feb 2023
DOIs
Publication statusPublished - 10 Mar 2023

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