Learning Biomechanically Plausible Human Motion from Sparse Radar Point Clouds

2026-08-04

AI Summary

This work addresses the frequent neglect of anatomical constraints in existing radar-based human pose estimation methods, which often yields biomechanically implausible motions. To enhance physical plausibility and subject-specific adaptation, the authors propose an end-to-end differentiable framework that integrates a biomechanical skeleton model and differentiable forward kinematics into radar point cloud processing. The approach incorporates kinematic supervision, individualized geometric parameter fitting, temporal pose prediction, and contact classification loss. Evaluated on rehabilitation movements from 11 subjects, the system achieves an average joint position error of 6.46 cm, a mean joint angle error of 8.08°, a contact classification F1 score of 0.935, and a limb scaling error of 3.4%, significantly outperforming current state-of-the-art methods.

Abstract

Radar-based human pose estimation has focused on improving learning algorithms while representing the body as unconstrained keypoint coordinates. We address the underexplored dimension of anatomical fidelity by integrating a full-body skeletal model into a differentiable, end-to-end trainable radar-based pose estimation framework, in which the pose network is supervised through forward kinematics while subject-specific geometry is fitted beforehand. Subject-specific body segment proportions are predicted from radar point cloud features to scale a biomechanical skeleton. A motion prediction network maps temporal radar sequences to generalized coordinates, and differentiable forward kinematics converts predicted joint angles into 3D positions. A contact classification loss encourages physically plausible foot-ground interaction. Under leave-one-subject-out cross-validation on 11 healthy participants performing rehabilitation exercises, the framework achieves 6.456 +/- 1.759 cm mean per-joint position error (MPJPE), 8.083 +/- 0.884 degrees mean per-joint angle error (MPJAE), 0.935 +/- 0.009 contact classification F1, and 3.4 +/- 1.3 % scaling error. This proof-of-concept study demonstrates the feasibility of recovering interpretable biomechanical descriptors from a single low-cost radar sensor in a controlled laboratory setting, a prerequisite for future clinical motion analysis.

Problem

• biomechanical plausibility
• radar point clouds
• human pose estimation
• anatomical fidelity
• motion analysis

Innovation

• biomechanical skeleton
• differentiable forward kinematics
• radar-based pose estimation
• subject-specific scaling
• contact classification