Transactions on Additive Manufacturing Meets Medicine
Vol 1 No S1 (2019): Trans. AMMM Supplement

Supplementary Abstracts

Assessing the performance of upper limb prostheses with virtual evaluation

Main Article Content

Chung Han Chua (Centre for Additive Manufacturing, University of Nottingham, Nottingham, United Kingdom), Ruth Goodridge (Centre for Additive Manufacturing, University of Nottingham, Nottingham, United Kingdom), Steve Benford (Mixed Reality Lab, University of Nottingham, Nottingham, United Kingdom), Ian Ashcroft (Centre for Additive Manufacturing, University of Nottingham, Nottingham, United Kingdom)


Motivation: Current upper-limb prostheses are generally unable to provide the functionality that amputees desire from their devices. Additive Manufacturing (AM) can produce bespoke prostheses that are adapted for individual needs. Despite significant advances in development of prostheses, there remains high abandonment rates. A contributing factor is a lack of effective evaluation methods to measure upper-limb prosthesis performance to aid design. Current standards of performance measurement using time-based tasks are inadequate, due to various limitations and oversimplified parameter space. This study aims to use virtual evaluation to assess the performance of prostheses for specific tasks to drive design guidelines.

Materials and Methods: A virtual environment was created to assess the grasping performance of five prosthetic devices: eNable Raptor-Hand, Clamp-Hand, Split-Hook, Boreham-Hand and Handii Hackberry. Using virtual evaluation removes user bias and allows a more standardised procedure. Simulations of the Southampton Hand Assessment Procedure are partially performed and the grasp quality is assessed by four numeric grasp metrics; these are calculated from the contact points, contact vectors, contact forces and contact torques. This provides an in-depth quantitative measure which can be optimised to improve the prosthesis design.

Results and Discussion: The average grasping performance scores for the prostheses were: Clamp-Hand (0.50), Split-Hook (0.47), Hackberry (0.41), Boreham-Hand (0.29) and Raptor-Hand (0.29). Design complexity was not correlated with improved performance. Anthropomorphic designs resulted in unstable grasps as multiple digits created unfavourable force vectors. Thumb position and digit abduction angles were predominantly chosen as aesthetic features rather than for efficacy.

Conclusions: AM can enable a wider range of prostheses with specific design features for an intended user. However, without a quantitative metric to optimise, it can be difficult to use AM to its full potential. Virtual evaluation can be used to assess the performance of prostheses numerically and can provide feedback to improve the design.

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