Selective Classification for Improved Robustness of Myoelectric Control Under Nonideal Conditions

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical p...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2011-06, Vol.58 (6), p.1698-1705
Main Authors: Scheme, Erik J., Englehart, Kevin B., Hudgins, Bernard S.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Amputee
Amputees - rehabilitation
Analysis of Variance
Applied sciences
Artificial Intelligence
Artificial Limbs
Biological and medical sciences
Boundaries
Classification
Classification algorithms
Computerized, statistical medical data processing and models in biomedicine
Discriminant Analysis
Electrodiagnosis. Electric activity recording
electromyogram (EMG)
Electromyography
Electromyography - methods
Exact sciences and technology
Feature extraction
Hand Strength
Humans
Information, signal and communications theory
Investigative techniques, diagnostic techniques (general aspects)
Medical management aid. Diagnosis aid
Medical sciences
Meetings
Movement - physiology
myoelectric
Myoelectric control
myoelectric signal
Nervous system
Pattern recognition
Pattern Recognition, Automated - methods
prostheses
Prosthesis Design
Prosthetics
Real time
Robustness
Signal processing
Signal Processing, Computer-Assisted
Support vector machines
Surgical implants
Telecommunications and information theory
Training
Wrist - physiology
Citations: Items that this one cites
Items that cite this one
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control system based on a selective multiclass one-versus-one classification scheme, capable of rejecting unknown data patterns, is introduced. This scheme is shown to outperform nine other popular classifiers when compared using conventional classification accuracy as well as a form of leave-one-out analysis that may be more representative of real prosthetic use. Additionally, the classification scheme allows for real-time, independent adjustment of individual class-pair boundaries making it flexible and intuitive for clinical use.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2011.2113182