SignFi is a publicly available CSI dataset specifically designed for Wi-Fi-based sign language recognition, capturing hand gesture signs collected in both home and laboratory environments using commodity Wi-Fi hardware. It is significant to the field because it provides a standardized benchmark for evaluating gesture and sign language recognition systems, enabling reproducible comparisons across different sensing algorithms and generalizability studies. The dataset includes variants across different environments and user subjects, making it particularly valuable for assessing cross-environment and cross-user generalization, which are critical challenges in Wi-Fi sensing research.
Source Papers
- A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels ↗ — A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Cha
- A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects ↗ — A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techni
- A survey on CSI-based Wi-Fi sensing datasets and models with a focus on reproducibility ↗ — A survey on CSI-based Wi-Fi sensing datasets and models with
- Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing ↗ — Context-Aware Predictive Coding: A Representation Learning F
- Deep Learning-Enhanced Human Sensing with Channel State Information: A Survey ↗ — Deep Learning-Enhanced Human Sensing with Channel State Info
- WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing ↗ — WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activi