Millimeter-wave Imaging for Anthropometric Body Measurement

AI in healthcare

Published: arXiv: 2605.23064v1

Authors

Miriam Senne Benjamin D. Killeen Christoph Baur Nassir Navab Azade Farshad

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.

Paper Summary

Problem

The main problem this paper addresses is the need for a fast, contactless, and privacy-preserving way to measure anthropometric body measurements, such as waist circumference, in clinical settings. Current methods, like manual tape measurements and optical scanners, require undressing and can be time-consuming, compromising dignity and excluding older adults and people with limited mobility.

Key Innovation

The key innovation of this paper is the use of millimeter-wave (mmWave) radar imaging to recover 3D human shape and extract anthropometric measurements from volumetric data. This method leverages a parametric body model (SMPL) fitted directly to the noisy mmWave point cloud, using a weighted registration pipeline and a vertex-weighting strategy to modulate a Chamfer energy function for reliable surface alignment and noise elimination.

Practical Impact

This research has significant practical implications for clinical settings, where frequent and accurate anthropometric measurements are essential for risk stratification, treatment planning, and monitoring. The proposed method enables fast, privacy-preserving, and contactless body measurements through clothing, without cameras or disrobing, and with minimal cooperation. This can broaden access to anthropometric measurements, reduce selection bias, and enable repeated assessments in clinics and care facilities for patients of all ages and mobility levels.

Analogy / Intuitive Explanation

Imagine trying to take a precise measurement of your waist circumference using a tape measure while wearing a loose-fitting shirt. It's challenging, right? That's because the tape measure can't accurately capture the shape of your body through the fabric. Millimeter-wave radar imaging is like a special kind of "X-ray vision" that can see through clothing and provide a precise 3D image of your body shape, allowing for accurate measurements without the need for disrobing or cumbersome equipment.

Paper Information

Categories:

cs.CV cs.LG

Published Date:

arXiv ID:

2605.23064v1

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