sulworm

sulworm / mmwave-human-pose-estimation

Public

mmWave radar-based human pose estimation project

19
0
100% credibility
Found May 07, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

Toolkit to align mmWave radar point clouds with IMU body poses, prepare datasets, train neural networks for radar-based human pose estimation, run predictions, and visualize results.

How It Works

1
🔍 Discover radar pose magic

You find a tool that turns invisible radar scans into 3D human body positions, perfect for privacy-friendly motion tracking.

2
📦 Gather your movement recordings

Collect paired data from a radar sensor capturing point clouds and a body tracker noting exact joint positions over time.

3
🔄 Perfectly align radar and body data

Match the radar clouds precisely with body positions by rotating and centering them, so everything lines up smoothly.

4
✂️ Slice into learning sequences

Chop the aligned recordings into short clips of movements, ready for the system to study patterns.

5
🧠 Train your pose predictor

Feed the sequences to teach the system how to guess full body poses just from radar points – it gets smarter with each lesson!

6
🎯 Predict on fresh recordings

Apply your trained predictor to new radar data and get back estimated body positions.

👏 Watch bodies come alive accurately

View animated 3D skeletons from radar alone, matching real movements beautifully for your applications.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 19 to 19 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is mmwave-human-pose-estimation?

This Python project delivers an end-to-end pipeline for mmwave human pose estimation, converting raw mmWave radar point clouds into 13-joint human skeletons using IMU data for ground truth. It handles data alignment between radar and IMU sensors, dataset preparation with sequence sampling, model training via PointNet encoders and transformers, and inference on new radar streams. Developers get CLI tools to process radar captures into aligned datasets, train pose models, run predictions, and visualize results in 3D animations—ideal for mmwave radar-based sensing in smart homes or patient monitoring.

Why is it gaining traction?

In a field crowded with vision-based pose trackers, this stands out for privacy-preserving mmwave radar pose estimation without cameras, aligning with recent advances in mmwave radar based sensing for autonomous vehicles and IoT. The auto-rotation and centroid alignment for radar-IMU sync saves hours of manual calibration, while built-in metrics like MPJPE and foot motion ratios help tune models fast. Niche hooks like single mmwave radar based pose detection draw devs exploring github mmwave radar projects beyond gesture recognition.

Who should use this?

Radar researchers prototyping mmwave human pose estimation for non-intrusive patient monitoring or secure mmwave radar based speaker verification in smart homes. Embedded devs on ti mmwave github hardware targeting edge computing with tiny radar mmwave radar based human activity classification. Teams in industry needing radar-only alternatives to cameras for autonomous vehicles or smart glasses eye tracking.

Verdict

Solid prototype for mmwave pose experiments at 19 stars and 1.0% credibility score—docs cover the pipeline but lack extensive tests or pre-trained models. Grab it if you're diving into radar sensing; otherwise, wait for more polish.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.