HelpMeWalk - A new digital process for orthoses production: Data processing, Workflow and Accuracy

The HelpMeWalk project introduces a low-cost, magnetic field–based 3D acquisition system using sensor- embedded textile bandages for digital modeling of lower leg orthoses. The system captures approximately 300–500 spatial points within six seconds, providing data for a model-based surface reconstruction. A multi-step workflow – comprising error cleaning, alignment of measured points to a model surface and form adjustment – enables accurate reconstruction of individual limb geometries. Prototype evaluation demonstrated robust functionality and a final mean surface deviation of 2.89 mm. Remaining inaccuracies were mainly linked to sparse sensor coverage and sensor noise. Future development will focus on optimizing sensor placement protocols, mobile App integration, and machine learning enhanced error detection.

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Publication Details

Journal
OJS
Published
2026-03-27
DOI
https://doi.org/10.60643/urai.v2025p26
Primary Topic
Prosthetics and Rehabilitation Robotics
Type
article
Field-Weighted Citation Impact
0.00
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article

HelpMeWalk - A new digital process for orthoses production: Data processing, Workflow and Accuracy

U. Tronnier, Corentin Féry, Maximilian Peter Mock, Dua Shahid et al.
OJS
Prosthetics and Rehabilitation Robotics
article

HelpMeWalk - A new digital process for orthoses production: Data processing, Workflow and Accuracy

U. Tronnier, Corentin Féry, Maximilian Peter Mock, Dua Shahid, Fabien Wilhelm
article en

Abstract

The HelpMeWalk project introduces a low-cost, magnetic field–based 3D acquisition system using sensor- embedded textile bandages for digital modeling of lower leg orthoses. The system captures approximately 300–500 spatial points within six seconds, providing data for a model-based surface reconstruction. A multi-step workflow – comprising error cleaning, alignment of measured points to a model surface and form adjustment – enables accurate reconstruction of individual limb geometries. Prototype evaluation demonstrated robust functionality and a final mean surface deviation of 2.89 mm. Remaining inaccuracies were mainly linked to sparse sensor coverage and sensor noise. Future development will focus on optimizing sensor placement protocols, mobile App integration, and machine learning enhanced error detection.

OJS
University of Applied Sciences Kaiserslautern (DE)
Openalex Percentile: Top 5%
Prosthetics and Rehabilitation Robotics
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