Document Type

Report

Date

6-16-1994

Embargo Period

5-7-2012

Keywords

Dexterous Hand, Phantom Limbs, Statistical Analysis, Neural Networks, Classification

Language

English

Disciplines

Computer Sciences

Description/Abstract

We analyze techniques for myoelectrical signals classification for the purpose of designing a multifunctional prosthetic device for human amputees. The main advantage of our system over existing models is that it is more robust, easier to work with, more general, and efficient enough to run in real time. We achieve this with the help of "Supervised Growing Cell Structures." an artificial neural network model designed by Fritzke [10]. The current paper focuses on the flexion of the index, middle and ring fingers, as these are the most difficult movements to tackle.

Additional Information

School of Computer and Information Science, Syracuse University, SU-CIS-94-3

Source

local

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