Hands can

Hands can: Determining the location and range of motion of digital joints in 3D scans

Dijkstra, Timo Johannes (TU Delft Industrial Design Engineering)

Geraedts, Jo (mentor)
Wu, Jun (graduation committee)
van der Zwan, Pieter (graduation committee)

Delft University of Technology

Integrated Product Design | Medisign

Curatio

2018-09-04

The versatility of the hands is revealed in its movements, but often not noticed before trauma occurs. Joint range of motion is used as a measure to follow the progress of diseases. A digital workflow for 3D data in medical appliances is envisioned for years.
The aim of this research is to develop a method that reliably and reproducability determine the range of motion of the digits. In current practice, the angles are measured using a goniometer. This method is very imprecise. Three methods to determine the location of joints in 3D hand scans can be distinguished: using heuristics, computer vision, and deep learning. Of those, deep learning is the most flexible, modern and accurate method and is therefore applied. The end result is a matrix containing the range of motion per joint and is applied to anatomically correctly manipulate a 3D model. For ease of manipulation, a physical manipulator is proposed. The results of this novel method show lower interrater differences than measurements with a goniometer.

Splint
3D scan
Orthosis
Deep learning
3D print
Arduino

http://resolver.tudelft.nl/uuid:11bd3b93-e47c-4eb7-8d2d-9b7a0dc34962

Student theses

master thesis

© 2018 Timo Johannes Dijkstra