Classification of raw myoelectric signals using finite impulse response neural networks

被引：0

作者：

Atsma, WJ ^{[1
]}

Hudgins, B ^{[1
]}

Lovely, DF ^{[1
]}

机构：

[1] Univ New Brunswick, Inst Biomed Engn, Fredericton, NB E3B 5A3, Canada

来源：

PROCEEDINGS OF THE 18TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOL 18, PTS 1-5 | 1997年 / 18卷

关键词：

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

A method for classifying movement patterns of the upper arm, intended for multifunction control of arm prostheses, is presented. A finite impulse response neural network (FIRNN) is trained on 100 msec segments of myoelectric signals (MES) recorded during the very initial stage of elbow flexion (FL) and extension (EX). The network develops a clear internal representation of the input signals and is capable of classifying them.

引用

页码：1474 / 1475

页数：2

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