arXiv (OAI Expanded)
open access
XAI2CSI: Interpreting CSI with eXplainable AI for Human Activity Recognition
Wi-Fi Channel State Information (CSI) has emerged as a key enabler for device-free Human Activity Recognition (HAR), enabling low-cost, unobtrusive sensing using existing communication infrastructure. However, Deep Learning (DL) models trained on CSI data often struggle to generalize across users, e
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- XAI2CSI: Interpreting CSI with eXplainable AI for Human Activity Recognition#819066
- Improving BLE-Based Passive Human Sensing with Deep Learning#925298
- Improving BLE-Based Passive Human Sensing with Deep Learning#921062
- More Features Are Not More Evidence: Limits of Training-Free Human Activity Recognition with Jev#1130996
- CIG-MAE: Cross-Modal Information-Guided Masked Autoencoder for Self-Supervised WiFi Sensing#1036604
Record · ID 809074
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive