Human facial neural activities and gesture recognition for machine-interfacing applications.
Int J Nanomedicine
; 6: 3461-72, 2011.
Article
em En
| MEDLINE
| ID: mdl-22267930
ABSTRACT
The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
/
Eletromiografia
/
Face
/
Expressão Facial
/
Sistemas Homem-Máquina
Limite:
Humans
Idioma:
En
Revista:
Int J Nanomedicine
Ano de publicação:
2011
Tipo de documento:
Article
País de afiliação:
Malásia