LightPoseNet is a real-time wireless perception system that estimates 3D human pose using commodity WiFi Channel State Information (CSI).
This project explores low-cost, privacy-preserving alternatives to camera-based perception for robotics, smart environments, and human-aware autonomous systems.
Unlike vision-based systems, LightPoseNet:
- Works through walls
- Preserves visual privacy
- Operates in low-light or occluded environments
- Runs in real time (4.1 ms inference)
- Uses only 0.29M parameters
Most robotic perception systems rely heavily on cameras or LiDAR. However:
- Cameras raise privacy concerns
- Visual systems fail under occlusion or poor lighting
- Hardware costs can scale quickly
WiFi signals are already present in most indoor environments.
This work investigates whether wireless channel distortions caused by human motion can be decoded into meaningful 3D pose information.
LightPoseNet demonstrates that commodity WiFi infrastructure can function as a spatial sensing layer for intelligent systems.
The full pipeline consists of:
WiFi CSI Acquisition → Signal Preprocessing → Feature Representation → Lightweight CNN → 3D Pose Regression
- Raw Channel State Information extraction
- Noise filtering and normalization
- Temporal window segmentation
- Lightweight convolutional architecture
- Squeeze-and-Excitation (SE) attention variant
- Optimized for low parameter count and fast inference
- Direct regression of 3D joint coordinates
- Evaluation using MPJPE and PCK metrics
| Model | MPJPE (mm) | PCK@0.10 | Params (M) | Inference (ms) |
|---|---|---|---|---|
| Baseline | 40 | 92.88% | 0.13 | 2.8 |
| SE-Enhanced | 40 | 93.31% | 0.29 | 4.1 |
- ~300× parameter reduction compared to prior CSI-based approaches
- Real-time inference suitable for edge deployment
- Competitive accuracy with significantly lower computational cost
This project uses the publicly available WiPose dataset.
The dataset is not included in this repository due to licensing constraints.
Please download the dataset from the official WiPose source and place it in the appropriate directory before running experiments.
Clone the repository:
git clone https://github.com/DeveshKaushal-9/LightPoseNet-Lightweight-3D-Human-Pose-Estimation-from-WiFi-CSI.git
cd LightPoseNet-Lightweight-3D-Human-Pose-Estimation-from-WiFi-CSI
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## 🏅 Academic Recognition
- 🎤 **Oral Presentation** — MIT Undergraduate Research Technology Conference (URTC) 2025
- 🧠 **Poster Accepted** — AAAI 2026 Empowering Global South AI (EGSAI) Workshop, Singapore
- 🏆 **Finalist** — TechConnect’25 Research Poster Competition (ResCon), IIT Bombay
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## 📄 Citation
If you use this work, please cite:
**Lightweight Human Pose Estimation from WiFi Channel State Information Using Deep Neural Networks**
MIT URTC 2025
(PDF available in repository)
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## 👤 Author
**Devesh Kaushal**
B.Tech, Computer Science & Engineering
Research interests:
Wireless sensing, real-time perception, efficient deep learning, and AI systems for physical-world intelligence.
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## 📜 License
MIT License