Hands can

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

Master thesis (2018)

Authors

T.J. Dijkstra Industrial Design Engineering

Contributors

J. Wu (coach)
Faculty
Industrial Design Engineering
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Published Date

04-09-2018

Language

English

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Faculty

Industrial Design Engineering

Abstract

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.