Learning Biomechanically Plausible Human Motion from Sparse Radar Point Clouds

• URL: http://arxiv.org/abs/2608.03637v1
• Date: Tue, 04 Aug 2026 13:20:05 GMT
• Title: Learning Biomechanically Plausible Human Motion from Sparse Radar Point Clouds
• Abstract summary: We integrate a full-body skeletal model into a differentiable, end-to-end trainable radar-based pose estimation framework.<n>Subject-specific body segment proportions are predicted from radar point cloud features to scale a biomechanical skeleton.<n>A motion prediction network maps temporal radar sequences to generalized coordinates, and differentiable forward kinematics converts predicted joint angles into 3D positions.
• Score: 39.56983715336933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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