Abstract

Modern prosthetics largely relies upon visual data processing and implementation technologies such as 3D scanning, mathematical modeling, computer-aided design (CAD) tools, and 3D-printing during all stages from design to fabrication. Despite the intensive advancement of these technologies, once the prosthetic socket model is obtained by 3D scanning, its appropriate orientation and positioning remain largely the responsibility of an expert requiring substantial manual effort. In this paper, an automated orientation algorithm based on the adjustment of the 3D-model virtual anatomical axis of the tibia along with the vertical axis of the rectangular coordinates in three-dimensional space is proposed. The suggested algorithm is implemented, tested for performance and experimentally validated by explicit comparisons against an expert assessment.

Highlights

  • According to World Health Organization (WHO) statistics, up to one billion people in the world constituting about 15% of the total population live with certain form of disability, including approximately 200 million experiencing considerable difficulties in functioning that limit their participation in family and society life, with the domination of cases in the upper age group of ≥60 years old, where nearly every second a person experiences moderate and about 10% severe disabilities [1].Severe disabilities are often associated with lower limb amputations, with approximately 30,000 to40,000 amputations performed each year, and more than 1.5 million people living with a lost limb in the U.S alone, with the majority requiring access to lower limb prosthetics [2]

  • Three projections of the straight line on the xy, xz and zy planes were obtained for both automated and manually oriented models, and angles between these projections in each plane were used for the quantitative comparison between the two approaches, as summarized in in this work we focused on the lower limb stump orientation problem, the proposed algorithm appears to be a universal tool for the 3D model orientation

  • The algorithm contains two key verification conditions, with the first condition checking whether the virtual anatomical axis of the tibia coincides with the vertical Z-axis, while the second condition triggering if the model is positioned with the stump end up after orientation

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Summary

Introduction

According to World Health Organization (WHO) statistics, up to one billion people in the world constituting about 15% of the total population live with certain form of disability, including approximately 200 million experiencing considerable difficulties in functioning that limit their participation in family and society life, with the domination of cases in the upper age group of ≥60 years old, where nearly every second a person experiences moderate and about 10% severe disabilities [1].Severe disabilities are often associated with lower limb amputations, with approximately 30,000 to40,000 amputations performed each year, and more than 1.5 million people living with a lost limb in the U.S alone, with the majority requiring access to lower limb prosthetics [2]. According to World Health Organization (WHO) statistics, up to one billion people in the world constituting about 15% of the total population live with certain form of disability, including approximately 200 million experiencing considerable difficulties in functioning that limit their participation in family and society life, with the domination of cases in the upper age group of ≥60 years old, where nearly every second a person experiences moderate and about 10% severe disabilities [1]. Reasons for amputation vary considerably between regions and age groups, ranging from severe trauma, wounds and burns caused by road traffic and occupational injuries as well as violence and humanitarian crises to tumors and inflammatory diseases potentially leading to the. Sci. 2020, 10, 3253 development of sepsis and multiple organ failure, on average around one half of amputation cases can be attributed to either trauma or disease, respectively [1,4]

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