🛜 ESPectre 👻 - Motion detection system based on Wi-Fi spectre analysis (CSI), with Home Assistant integration.
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Updated
Dec 8, 2025 - C
🛜 ESPectre 👻 - Motion detection system based on Wi-Fi spectre analysis (CSI), with Home Assistant integration.
A list of awesome papers and cool resources on WiFi CSI sensing.
Extract Channel State Information from WiFi-enabled ESP32 Microcontroller. Active and Passive modes available. (https://stevenmhernandez.github.io/ESP32-CSI-Tool/)
test example of paper, Can WiFi Estimate Person Pose?
📊 Wi-Fi Channel State Information(CSI) visualization with python
[INFOCOM WKSHPS 2024] Official Repository for The Paper, Finding the Missing Data: A BERT-inspired Approach Against Package Loss in Wireless Sensing
The Pytorch implementation of "RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing"
[IEEE IOT-J] Official Repository for The Paper, CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network
[ICCC 2025] Official Repository for The Paper, KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment
A framework for fall detection with CSI Wi-Fi data
This refers to the paper multi-modal pose estimation in XR applications leveraging ISAC
This contains the plug and play code for "mmHSense: Multi-Modal and Distributed mmWave ISAC Datasets for Human Sensing"
This is the ESPHome external component for TOMMY, allowing you to add Wi-Fi motion detection capabilities to your existing ESPHome devices.
Context Aware Predictive Coding (CAPC)
This dataset refers to the paper https://arxiv.org/abs/2306.17062. The dataset consists of beam SNR samples corresponding to set of 10 gestures across three people and two environments
This is the Home Assistant add-on for TOMMY, a Wi-Fi motion detection system that turns ESP32 devices into motion sensors capable of detecting movement through walls and obstacles.
A Realtime Wi-Fi Sensing System Demo
Evaluating the Generalisability of Segmentation Methods in Wifi-Sensing
WiFi CSI-based gesture recognition using dual-path ensemble deep learning (CNN2D + CNN1D-LSTM). 90.19% accuracy on ESP32 hardware with 426K parameters.
This refers to the dataset of the paper CSI4Free: GAN-Augmented mmWave CSI for Improved Pose Classification
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