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WiFi sensing has been evolving rapidly in recent years. Empowered bypropagation models and deep learning methods, many challenging applications arerealized such as WiFi-based human activity recognition and gesture recognition.However, in contrast to deep learning for visual recognition and naturallanguage processing, no sufficiently comprehensive public benchmark exists. Inthis paper, we review the recent progress on deep learning enabled WiFisensing, and then propose a benchmark, SenseFi, to study the effectiveness ofvarious deep learning models for WiFi sensing. These advanced models arecompared in terms of distinct sensing tasks, WiFi platforms, recognitionaccuracy, model size, computational complexity, feature transferability, andadaptability of unsupervised learning. It is also regarded as a tutorial fordeep learning based WiFi sensing, starting from CSI hardware platform tosensing algorithms. The extensive experiments provide us with experiences indeep model design, learning strategy skills and training techniques forreal-world applications. To the best of our knowledge, this is the firstbenchmark with an open-source library for deep learning in WiFi sensingresearch. The benchmark codes are available athttps://github.com/xyanchen/WiFi-CSI-Sensing-Benchmark.

SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing