In Progress

Human Pose Estimation

mmPose-FK: dynamic forward kinematics with deep learning for stable radar-based pose tracking

Overview

To enhance human pose estimation capabilities, we proposed mmPose-FK, a novel mmWave radar-based pose estimation method that utilizes a dynamic forward kinematics (FK) approach. This method is designed to overcome the challenges of low resolution, specularity, and noise artifacts common to mmWave radars, which often result in unstable joint poses. By integrating FK into a deep learning model, mmPose-FK provides more stable and accurate pose estimation, improving the reliability of radar-based human skeletal tracking.

Key Highlights

Healthcare Monitoring
Sports Analysis
Human-Computer Interaction
Rehabilitation
Skeletal Tracking

Methodology

Dynamic Forward Kinematics (FK) combined with deep neural networks for joint stability. Training on noisy, low-resolution radar signatures to achieve robust pose estimation across diverse environments and lighting conditions.