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Utilizing additive manufacturing and gamified virtual simulation in the design of neuroprosthetics to improve pediatric outcomes

Published online by Cambridge University Press:  05 August 2019

Albert Manero II Open the ORCID record for Albert Manero II [Opens in a new window] ,
Peter Smith Open the ORCID record for Peter Smith [Opens in a new window] ,
John Sparkman ,
Matt Dombrowski ,
Dominique Courbin ,
Paul Barclay  and
Albert Chi
Albert Manero II*
Affiliation:
Limbitless Solutions, University of Central Florida, 4217 E Plaza Drive, Orlando, FL 32816, USA
Peter Smith
Affiliation:
Limbitless Solutions, University of Central Florida, 4217 E Plaza Drive, Orlando, FL 32816, USA
John Sparkman
Affiliation:
Limbitless Solutions, University of Central Florida, 4217 E Plaza Drive, Orlando, FL 32816, USA
Matt Dombrowski
Affiliation:
Limbitless Solutions, University of Central Florida, 4217 E Plaza Drive, Orlando, FL 32816, USA
Dominique Courbin
Affiliation:
Limbitless Solutions, University of Central Florida, 4217 E Plaza Drive, Orlando, FL 32816, USA
Paul Barclay
Affiliation:
Department of Psychology, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816, USA
Albert Chi*
Affiliation:
Division of Trauma, Critical Care and Acute Care Surgery, Department of Surgery, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239, USA
*
Address all correspondence to Albert Manero II at [email protected]; Albert Chi at [email protected]
Address all correspondence to Albert Manero II at [email protected]; Albert Chi at [email protected]
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Abstract

Additive manufacturing used with custom electromyographic sensors has been demonstrated for neuroprosthetic limb manufacturing and is now translating to the clinical environment. These manufacturing methods have dramatically reduced device weight while increasing the capability for multi-finger dexterity. Using wearable electromyography sensors standalone from the prosthetic limb, a new virtual training method has been designed and tested to improve human–machine interaction. This type of training leverages real-time visual feedback to user inputs, supporting improved timing and magnitudes of muscle contractions. The combination of these technologies may provide a stronger affinity between the pediatric patient group and the device.

Type
Research Letters
Information
MRS Communications , Volume 9 , Issue 3 , September 2019 , pp. 941 - 947
Copyright
Copyright © The Author(s) 2019 

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