WiFi DensePose: WiFi-based human pose estimation
System for real-time human pose tracking using WiFi Channel State Information without cameras or wearables.
Learn more about WiFi DensePose
WiFi DensePose is a human pose estimation system that uses WiFi Channel State Information (CSI) data instead of cameras. The system processes radio frequency signals from standard WiFi routers to detect and track human body positions and movements in real-time. It implements machine learning models to analyze signal patterns and extract pose information with sub-50ms latency. The system supports multi-person tracking and includes specialized modules for healthcare monitoring and disaster response scenarios.
Privacy Preserving
Uses radio frequency signals instead of cameras, eliminating visual privacy concerns while maintaining pose tracking accuracy.
Through-Wall Detection
WiFi signals can penetrate walls and obstacles, enabling pose estimation in occluded environments where cameras cannot operate.
Rust Performance
High-performance Rust implementation achieves ~810x speedup over Python with 54,000 fps throughput and reduced memory usage.
from wifi_densepose import WiFiDensePose, Config
# Configure the system for real-time pose estimation
config = Config(
sampling_rate=1000, # 1kHz CSI sampling
num_antennas=4,
num_subcarriers=64,
latency_target=50, # Sub-50ms processing
max_persons=10 # Multi-person tracking
)
# Initialize the WiFi DensePose system
pose_estimator = WiFiDensePose(config)
# Start pose estimation with CSI data
csi_data = get_wifi_csi_data() # Your CSI data source
pose_results = pose_estimator.estimate_poses(csi_data)
# Access pose information
for person_id, pose in pose_results.items():
print(f"Person {person_id}: {len(pose.keypoints)} keypoints detected")
print(f"Confidence: {pose.confidence:.2f}")
print(f"Activity: {pose.activity_type}")Related Repositories
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