HandAnthro: Automated Hand Anthropometry from a Single Image

2026-09-29

AI Summary

This study addresses the limited scalability of traditional hand anthropometry in protective glove design, which typically requires specialized equipment and manual annotation. We propose a low-cost, automated smartphone-based measurement method that extracts 44 ergonomic dimensions from a single palm photograph. The approach leverages standard paper for geometric calibration, combined with image whitening, contour analysis, and an enhanced YOLO pose estimation model. Controlled experiments demonstrated a mean absolute error of only 3.80 mm with a 97.8% success rate. Furthermore, field testing successfully acquired complete datasets for 260 firefighters, validating the feasibility of achieving high-precision, large-scale hand anthropometry without dedicated hardware.

Abstract

Hand anthropometry supports protective-glove design, but existing measurement methods often require trained operators, specialized hardware, or manual landmarking. We present HandAnthro, which estimates 44 projected hand dimensions from a smartphone photograph of a palm-up hand on US letter-size paper. The pipeline reconstructs wrist-occluded paper boundaries for rectification, whitens non-hand pixels, and refines 41 anthropometry-specific landmarks from a fine-tuned You Only Look Once (YOLO) pose model using image-specific geometry and contours. Controlled evaluation comprised 720 captures from 45 held-out participants, each contributing 16 images across two smartphones, two backgrounds, two angles, and two nominal illumination settings. HandAnthro produced complete outputs for 704 captures (97.8%); among these, mean absolute error (MAE) was 3.80 mm per dimension against two trained operators' caliper measurements. Regional MAEs were 2.48 mm for non-thumb fingers, 6.04 mm for thumbs, and 6.17 mm for palm and wrist. In a researcher-assisted mobile-app pilot, automated batch processing returned all 44 dimensions for 260 of 268 retained, researcher-screened firefighter images (97.0%). A descriptive, unpaired comparison with an independent national firefighter reference yielded a mean absolute difference of 2.40 mm across 28 sex-by-dimension group-mean contrasts. These results characterize controlled measurement performance and researcher-assisted field feasibility for future distributed hand-anthropometry studies.

Problem

• hand anthropometry
• automated measurement
• single image
• hand dimensions
• protective-glove design

Innovation

• Hand Anthropometry
• Single Image Estimation
• YOLO Pose Model
• Landmark Refinement
• Image Rectification