Millimeter-wave Imaging for Anthropometric Body Measurement

2026-05-21

AI Summary

This study addresses the limitations of traditional anthropometric methods—such as low efficiency, privacy concerns, and inaccessibility for individuals with limited mobility—by proposing a contactless human body measurement approach based on millimeter-wave radar. The method captures full-body 3D point clouds through clothing and employs a vertex-weighted Chamfer energy function, integrating foot-grounding constraints and pose priors to drive robust registration of the SMPL parametric body model. This enables high-fidelity shape recovery even from noisy point cloud data. Requiring neither disrobing nor visual cameras and minimal user cooperation, the approach accurately estimates key anthropometric metrics—including waist-to-hip ratio, limb circumferences, and torso dimensions—and is suitable for individuals across all age groups and levels of physical ability.

Abstract

Body shape and circumferences are clinically informative biomarkers for risk stratification, including measures such as waist to hip ratio, limb and trunk girths, yet conventional tools such as manual tape measures and optical scanners often require undressing and sustained poses. These demands slow workflows, compromise dignity, and exclude many older adults and people with limited mobility. To make measurement fast and contactless, we leverage millimeter-wave (mmWave) radar, which preserves privacy and operates through typical clothing, enabling quick full-body acquisition. In this work, we present a new optimization-based framework to recover 3D human shape and extract a comprehensive set of anthropometric measurements from volumetric mmWave data. Our method introduces a weighted registration pipeline that fits a parametric body model (SMPL) directly to the noisy mmWave point cloud. The core of our contribution is a vertex-weighting strategy that modulates a Chamfer energy function for reliable surface alignment and noise elimination. We further stabilize the fit by incorporating a foot-ground plane constraint and pose priors, optimizing directly for the SMPL parameters. Together, these components enable a fast, privacy preserving workflow that delivers high fidelity body shape and measurements through clothing without cameras or disrobing and with minimal cooperation, supporting frequent risk oriented assessments in clinics and care facilities for patients of all ages and mobility levels.

Problem

• anthropometric measurement
• body shape
• millimeter-wave imaging
• contactless sensing
• clinical assessment

Innovation

• millimeter-wave radar
• 3D human shape reconstruction
• SMPL model fitting
• Chamfer distance optimization
• privacy-preserving anthropometry