Adapting myoelectric control in real-time using a virtual environment.
J Neuroeng Rehabil
; 16(1): 11, 2019 01 16.
Article
em En
| MEDLINE
| ID: mdl-30651109
ABSTRACT
BACKGROUND:
Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device.METHODS:
Here we present an engaging and interactive virtual reality environment for optimal training of a myoelectric controller. Using this tool, we evaluated the importance of training a classifier actively (i.e., moving the residual limb during data collection) compared to passively (i.e., maintaining the limb in a single, neutral orientation), and whether computational adaptation through serious gaming can improve performance.RESULTS:
We found that actively trained classifiers performed significantly better than passively trained classifiers for non-amputees (P < 0.05). Furthermore, collecting data passively with minimal instruction, paired with computational adaptation in a virtual environment, significantly improved real-time performance of myoelectric controllers.CONCLUSION:
These results further support previous work which suggested active movements during data collection can improve pattern recognition systems. Furthermore, adaptation within a virtual guided serious game environment can improve real-time performance of myoelectric controllers.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Membros Artificiais
/
Reconhecimento Automatizado de Padrão
/
Eletromiografia
/
Aprendizado de Máquina
/
Realidade Virtual
Tipo de estudo:
Clinical_trials
Limite:
Adult
/
Female
/
Humans
/
Male
Idioma:
En
Revista:
J Neuroeng Rehabil
Assunto da revista:
ENGENHARIA BIOMEDICA
/
NEUROLOGIA
/
REABILITACAO
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos