# Customized Orthosis Design Based on Surface Reconstruction from 3D-Scanned Points

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## Abstract

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## 1. Introduction

## 2. Literature Review

## 3. Proposed Methodology

#### 3.1. Calculation of the Base Plane

- Determination of the orientation of the estimated 3D surface

- Determination of the base plane

#### 3.2. Determination of the Approximated B-Spline Curves

- B-Spline curve approximation

#### 3.3. Interpolation of the B-Spline Surface

- Choose a fixed number $\alpha $ of points to be selected from all the reconstructed curves.
- Generate a perpendicular plane ${F}_{p}$ to the base plane.
- Given the barycenter of each approximated curve, the plane ${F}_{p}$ is rotated using a calculated angle $\alpha $ based on the number $\alpha $ of selected points.
- The selected points on each curve are obtained by the intersection of the rotated plane with the corresponding B-Spline curve.
- Interpolate the final B-Spline surface given all the selected points and the number of approximated curves.

## 4. Experimental Results on Quality Assessment

^{−6}mm root mean square error resulting from the reconstruction of regular feature models in [26].

## 5. Surface Reconstruction Results

#### 5.1. Surface Reconstruction for Customized Finger Orthosis Design

^{−7}mm. Using the same input data, the surface is reconstructed using Ben Makhlouf’s method [22]. An MRMS error of 1.006 × 10

^{−6}mm is obtained.

#### 5.2. Surface Reconstruction for Customized Orthosis Design of Part of Foot

^{−6}mm. The reconstruction error of the same input surface using Ben Makhlouf’s method [22] is 2.43 × 10

^{−5}mm.

#### 5.3. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Method | Differences | Limitations |
---|---|---|

Hoppe et al. [13] | Approximated 3D surface reconstruction from unorganized 3D point cloud | Need for improvement in method accuracy |

Commean et al. [7] | Multiple camera setup for simultaneous capture of the lower limb | Complex process and difficult to achieve high accuracy |

Dinh et al. [14] | Surface reconstruction technique based on tensor field-driven anisotropic basis functions | Captures sharp features, no need for prior knowledge about surface topology, moderate accuracy (average Euclidean distance error of 0.0120) |

Carr et al. [15] | Surface reconstruction using radial basis functions (RBF) and hole-filling | Efficient and accurate reconstruction, especially for large datasets |

Hornung and Kobbelt [16] | Unsigned distance function-based surface reconstruction with resilience to noise | Reconstruction without normal information, resilience to misalignment noise |

Alliez et al. [17] | Voronoi algorithm-based surface reconstruction using surface normal computation | Surface normal and tensor field computation using Voronoi diagram |

Huang et al. [18] | Weighted locally optimal projection and principal component analysis for surface reconstruction | Denoising of input 3D point cloud, normal estimation, priority-guided normal propagation, moderate accuracy |

Mahmood et al. [10] | Surface reconstruction from video image data using a pinhole camera | Complex process based on video frames, challenging to achieve high accuracy |

Rouhani et al. [19] | Implicit B-Spline surface-based reconstruction algorithm | No parameterization required, solving a system of linear equations |

Louhichi et al. [21] | Weighted displacement estimation-based surface reconstruction for deformed mesh | Improved algorithm for control point approximation in B-Spline surface reconstruction, comparison of error with existing methods |

Makhlouf et al. [22] | Enhanced weighted displacement estimation-based surface reconstruction algorithm for deformed mesh | Improved control point approximation in B-Spline surface reconstruction, comparison with existing methods for efficiency validation |

Venkateswaran et al. [11] | Microsoft Kinect sensor-based 3D reconstruction method using RGB and depth images | Significant reconstruction errors |

Chaparro-Rico et al. [12] | 3D scan of the limb using MATLAB software, boundary surface generation using SolidWorks software | Accuracy not specified |

Overall | Ongoing improvement in the quality of resulting surfaces | Current methods need further progress in result robustness and accuracy to meet medical device design requirements |

Number of Points | Reconstruction Error (mm) | |
---|---|---|

1st case | 6294 | 0.06821 × 10^{−6} |

2nd case | 6365 | 3.204 × 10^{−6} |

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## Share and Cite

**MDPI and ACS Style**

Alrasheedi, N.H.; Ben Makhlouf, A.; Louhichi, B.; Tlija, M.; Hajlaoui, K.
Customized Orthosis Design Based on Surface Reconstruction from 3D-Scanned Points. *Prosthesis* **2024**, *6*, 93-106.
https://doi.org/10.3390/prosthesis6010008

**AMA Style**

Alrasheedi NH, Ben Makhlouf A, Louhichi B, Tlija M, Hajlaoui K.
Customized Orthosis Design Based on Surface Reconstruction from 3D-Scanned Points. *Prosthesis*. 2024; 6(1):93-106.
https://doi.org/10.3390/prosthesis6010008

**Chicago/Turabian Style**

Alrasheedi, Nashmi H., Aicha Ben Makhlouf, Borhen Louhichi, Mehdi Tlija, and Khalil Hajlaoui.
2024. "Customized Orthosis Design Based on Surface Reconstruction from 3D-Scanned Points" *Prosthesis* 6, no. 1: 93-106.
https://doi.org/10.3390/prosthesis6010008