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

Jianfei Yang¹, Xinyan Chen¹, Han Zou¹

¹School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore.

Patterns (New York, N.Y.)

|March 24, 2023

Summary

This summary is machine-generated.

A new benchmark library, SenseFi, is introduced for WiFi sensing research. It provides an open-source tool for evaluating deep learning models across various tasks and platforms, enhancing human-sensing applications.

Keywords:

WiFi sensing activity recognition benchmarking channel state information deep learning gesture recognition human sensing transfer learning ubiquitous computing unsupervised learning

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Area of Science:

Computer Science
Signal Processing
Artificial Intelligence

Background:

WiFi sensing is advancing for privacy-preserving human-sensing using signal processing and deep learning.
A public benchmark for deep learning in WiFi sensing is currently lacking, hindering research progress.

Purpose of the Study:

To introduce SenseFi, a comprehensive benchmark and open-source library for deep learning in WiFi sensing.
To evaluate deep learning models on diverse sensing tasks, WiFi platforms, and performance metrics.

Main Methods:

Review of recent advancements in WiFi sensing hardware and algorithms.
Development and implementation of the SenseFi benchmark library.
Extensive experimental evaluation of various deep learning models within SenseFi.

Main Results:

SenseFi provides a standardized platform for comparing deep learning models in WiFi sensing.
Experiments offer insights into model design, learning strategies, and training for real-world applications.
Evaluations cover recognition accuracy, model size, computational complexity, and feature transferability.

Conclusions:

SenseFi serves as a valuable, open-source tool for the WiFi sensing research community.
The benchmark facilitates the validation of learning-based WiFi-sensing methods across multiple datasets and platforms.
This work aids in advancing the development of robust and efficient deep learning models for WiFi sensing.