SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing
This provides a standardized evaluation framework for researchers in WiFi sensing, though it is incremental as it builds on existing methods without introducing new algorithms.
The authors tackled the lack of a comprehensive public benchmark for deep learning in WiFi sensing by proposing SenseFi, a library and benchmark that compares various models across tasks like human activity recognition, achieving recognition accuracies up to 95% in some experiments.
WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based human activity recognition and gesture recognition. However, in contrast to deep learning for visual recognition and natural language processing, no sufficiently comprehensive public benchmark exists. In this paper, we review the recent progress on deep learning enabled WiFi sensing, and then propose a benchmark, SenseFi, to study the effectiveness of various deep learning models for WiFi sensing. These advanced models are compared in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, feature transferability, and adaptability of unsupervised learning. It is also regarded as a tutorial for deep learning based WiFi sensing, starting from CSI hardware platform to sensing algorithms. The extensive experiments provide us with experiences in deep model design, learning strategy skills and training techniques for real-world applications. To the best of our knowledge, this is the first benchmark with an open-source library for deep learning in WiFi sensing research. The benchmark codes are available at https://github.com/xyanchen/WiFi-CSI-Sensing-Benchmark.