Yang, J., Chen, X., Zou, H., Lu, C. X., Wang, D., Sun, S., & Xie, L. (2023). SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing. Patterns, 4(3), 100703. https://doi.org/10.1016/j.patter.2023.100703
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 comprehensivepublic benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does notyet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensingalgorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluatevarious deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, modelsize, computational complexity, and feature transferability. Extensive experiments are performed whose re-sults provide valuable insights into model design, learning strategy, and training techniques for real-worldapplications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deeplearning in WiFi sensing research that offers researchers a convenient tool to validate learning-basedWiFi-sensing methods on multiple datasets and platforms.
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
There was no specific funding for the research done