Electronic Thesis and Dissertation Repository

Thesis Format

Monograph

Degree

Master of Engineering Science

Program

Mechanical and Materials Engineering

Supervisor

Tutunea-Fatan, O. Remus

2nd Supervisor

L. Ferreira

Abstract

The COVID-19 pandemic has significantly accelerated the adoption of telehealth and remote rehabilitation methods, necessitating innovative solutions for medical treatments that traditionally require in-person interactions. This thesis investigates the accuracy of different reverse engineering techniques used to create 3D printed splints for hand therapy, focusing on the comparison between photogrammetry and mobile scanning methods. The study employs a custom-built photogrammetry scanner using 42 Raspberry Pi cameras and a mobile phone setup to capture hand geometries. Both methods' workflows are meticulously developed and assessed for their ability to produce precise 3D models.

The accuracy of the scanning methods is evaluated using uniform and non-uniform objects, including a wooden cube and 3D printed models of a hand, cat, and ship. Surface deviation mapping is employed to compare the scanned models with their original digital counterparts. The thesis also compares the accuracy and fit of 3D printed splints generated from these scans with traditionally made thermoplastic splints crafted by therapists.

Results indicate that the photogrammetry scanner provides higher accuracy and consistency than the mobile scanning method. The 3D printed splints, generated using specialized CAD-based software, exhibit significantly lower deviation and better fit compared to traditional splints. This research highlights the potential of 3D printing technology in orthotic fabrication, offering improved patient outcomes and streamlined processes for remote hand therapy.

The findings underscore the viability of photogrammetry and mobile scanning methods in telehealth applications, suggesting that while mobile scanning offers convenience, further enhancements are needed to match the precision of photogrammetry.

Summary for Lay Audience

The COVID-19 pandemic has changed how we approach healthcare, making remote medical care more important than ever. This shift has highlighted the need for new ways to provide treatments that usually require face-to-face interaction, such as making custom splints for hand therapy.

In this study, we explored two methods for creating 3D models of hands to make these custom splints: using a photogrammetry scanner and a mobile phone camera. The photogrammetry scanner uses 42 small cameras to take pictures from all angles, while the mobile phone method relies on taking pictures with a regular smartphone. We wanted to see which method was more accurate in capturing the shape of a hand. To test this, we used simple shapes like a wooden cube and more complex shapes like 3D-printed models of a hand, a cat, and a ship. We then compared the 3D models created by both methods to the original shapes to see how closely they matched. Our results showed that the photogrammetry scanner was more accurate and reliable than the mobile phone method. We also compared the 3D-printed splints made from these scans to traditional splints made by therapists. The 3D-printed splints were more precise and fit better than the traditional ones.

This research shows that 3D printing technology can improve how we make custom splints, making the process faster and more accurate. While using a mobile phone for scanning is convenient, it still needs some improvements to match the accuracy of the photogrammetry scanner. In conclusion, our study suggests that advanced 3D scanning and printing techniques have the potential to revolutionize remote hand therapy, providing better outcomes for patients who need custom splints. Future work will focus on making these technologies even better so that more people can benefit from them.

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Available for download on Tuesday, September 01, 2026

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