ArticlePatterns (New York, N.Y.)2023
SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing.
Article in Patterns (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- DUAL-Net: Joint Domain-Invariant and User-Adaptive Feature Learning for Gesture Recognition.Sensors (Basel, Switzerland) · 2026Article
- Deep-Learning-Based Baseline Evaluation of Public WiFi CSI Datasets for Contactless RF-Based Human Activity Recognition.Sensors (Basel, Switzerland) · 2026Article
- A Benchmark Dataset for Radio Signal Image-based Person Re-Identification.Scientific data · 2025Article
- CRoP: Context-wise Robust Static Human-Sensing Personalization.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- Self-Supervised WiFi-Based Identity Recognition in Multi-User Smart Environments.Sensors (Basel, Switzerland) · 2025Article
- Enhanced Human Activity Recognition Using Wi-Fi Sensing: Leveraging Phase and Amplitude with Attention Mechanisms.Sensors (Basel, Switzerland) · 2025Article
- RGANet: A Human Activity Recognition Model for Extracting Temporal and Spatial Features from WiFi Channel State Information.Sensors (Basel, Switzerland) · 2025Article
- Commodity Wi-Fi-Based Wireless Sensing Advancements over the Past Five Years.Sensors (Basel, Switzerland) · 2024Review
- Toward ubiquitous sensing: Researchers turn WiFi signals into human activity patterns.Patterns (New York, N.Y.) · 2023Article
- TCS-Fall: Cross-individual fall detection system based on channel state information and time-continuous stack method.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.