TY - JOUR
T1 - SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing
AU - Yang, Jianfei
AU - Chen, Xinyan
AU - Zou, Han
AU - Lu, Chris Xiaoxuan
AU - Wang, Dazhuo
AU - Sun, Sumei
AU - Xie, Lihua
N1 - Funding Information:
This research is supported by NTU Presidential Postdoctoral Fellowship, “Adaptive Multi-modal Learning for Robust Sensing and Recognition in Smart Cities” project fund ( 020977-00001 ), at the Nanyang Technological University , Singapore.
Publisher Copyright:
© 2023 The Author(s)
PY - 2023/3/10
Y1 - 2023/3/10
N2 - 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.
AB - 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.
U2 - 10.1016/j.patter.2023.100703
DO - 10.1016/j.patter.2023.100703
M3 - Article
VL - 4
SP - 1
EP - 22
JO - Patterns
JF - Patterns
SN - 2666-3899
IS - 3
M1 - 100703
ER -