WiFi DensePose: Human Tracking Through Walls

Revolutionary WiFi-Based Human Pose Detection

AI can track your full-body movement through walls using just WiFi signals. Researchers at Carnegie Mellon have trained a neural network to turn basic WiFi signals into detailed wireframe models of human bodies.

System Status

API Server-

Hardware-

Inference-

Streaming-

Data Source-

System Metrics

CPU Usage

0%

Memory Usage

0%

Disk Usage

0%

Features

Live Statistics

Active Persons0

Avg Confidence0%

Total Detections0

Zone Occupancy

🏠

Through Walls

Works through solid barriers with no line of sight required

🔒

Privacy-Preserving

No cameras or visual recording - just WiFi signal analysis

⚡

Real-Time

Maps 24 body regions in real-time at 100Hz sampling rate

💰

Low Cost

Built using $30 commercial WiFi hardware

24Body Regions

100HzSampling Rate

87.2%Accuracy (AP@50)

$30Hardware Cost

Hardware Configuration

3×3 Antenna Array

Click antennas to toggle their state

Transmitters (3)

Receivers (6)

WiFi Configuration

Frequency 2.4GHz ± 20MHz

Subcarriers 30

Sampling Rate 100 Hz

Total Cost $30

Real-time CSI Data

Amplitude:

0.75

Phase:

1.2π

Live Demonstration

Start StreamStop Stream Ready

WiFi Signal Analysis

Signal Strength:-45 dBm

Processing Latency:12 ms

Human Pose Detection

Persons Detected:0

Confidence:0.0%

Keypoints:0/0

System Architecture

1

CSI Input

Channel State Information collected from WiFi antenna array

2

Phase Sanitization

Remove hardware-specific noise and normalize signal phase

3

Modality Translation

Convert WiFi signals to visual representation using CNN

4

DensePose-RCNN

Extract human pose keypoints and body part segmentation

5

Wireframe Output

Generate final human pose wireframe visualization

Performance Analysis

WiFi-based (Same Layout)

Average Precision:43.5%

AP@50:87.2%

AP@75:44.6%

Image-based (Reference)

Average Precision:84.7%

AP@50:94.4%

AP@75:77.1%

Advantages & Limitations

Advantages

• Through-wall detection
• Privacy preserving
• Lighting independent
• Low cost hardware
• Uses existing WiFi

Limitations

• Performance drops in different layouts
• Requires WiFi-compatible devices
• Training requires synchronized data

Real-World Applications

👴

Elderly Care Monitoring

Monitor elderly individuals for falls or emergencies without invading privacy. Track movement patterns and detect anomalies in daily routines.

Fall DetectionActivity MonitoringEmergency Alert

🏠

Home Security Systems

Detect intruders and monitor home security without visible cameras. Track multiple persons and identify suspicious movement patterns.

Intrusion DetectionMulti-person TrackingInvisible Monitoring

🏥

Healthcare Patient Monitoring

Monitor patients in hospitals and care facilities. Track vital signs through movement analysis and detect health emergencies.

Vital Sign AnalysisMovement TrackingHealth Alerts

🏢

Smart Building Occupancy

Optimize building energy consumption by tracking occupancy patterns. Control lighting, HVAC, and security systems automatically.

Energy OptimizationOccupancy TrackingSmart Controls

🥽

AR/VR Applications

Enable full-body tracking for virtual and augmented reality applications without wearing additional sensors or cameras.

Full Body TrackingSensor-freeImmersive Experience

Implementation Considerations

While WiFi DensePose offers revolutionary capabilities, successful implementation requires careful consideration of environment setup, data privacy regulations, and system calibration for optimal performance.

Model Training

Record CSI data, train pose estimation models, and manage .rvf files