343
Views
25
CrossRef citations to date
0
Altmetric
Original Articles

A review of existing anatomical data capture methods to support the mass customisation of wrist splints

, &
Pages 201-207 | Received 24 Sep 2010, Published online: 08 Nov 2010
 

Abstract

Anatomical data acquisition methods used within medicine exhibit various strengths and weaknesses, particularly with regards to accuracy, resolution, patient comfort and safety. Difficulties with data acquisition of wrist and hand geometry are often underestimated, and a suitable method is yet to be identified and standardised to capture skin surface topography to support the mass customisation of wrist splints. The aim of this investigation is to identify a suitable data acquisition method, capable of digitising collected data of the wrist and hand, for manipulation and conversion into a suitable file format to create customised wrist splints using additive manufacturing. Literature suggests that scanning inanimate objects such as plaster casts using multiple three-dimensional laser scanners can provide adequate quality scans with suitable accuracy and resolution, with low cost and low risk to the patient. However, post processing would be required to create a “watertight” digital model suitable for additive manufacturing.

Acknowledgements

Many thanks to Loughborough University for funding the research described. has been reproduced with the permission of Michael Raphael at Direct Dimensions, Inc. was kindly provided with the permission of Gavin Williams at Loughborough University. Special thanks to Ella Donnison and Lucia Ramsey for their help, advice and contribution.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access
  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart
* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.