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## 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 Persons 0
Avg Confidence 0%
Total Detections 0

#### 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

24 Body Regions
100Hz Sampling Rate
87.2% Accuracy (AP@50)
$30 Hardware 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

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 Detection Activity Monitoring Emergency Alert
🏠

### Home Security Systems

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

Intrusion Detection Multi-person Tracking Invisible Monitoring
🏥

### Healthcare Patient Monitoring

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

Vital Sign Analysis Movement Tracking Health Alerts
🏢

### Smart Building Occupancy

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

Energy Optimization Occupancy Tracking Smart Controls
🥽

### AR/VR Applications

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

Full Body Tracking Sensor-free Immersive 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