Flexible Neural Trees for Online Hand Gesture Recognition using Surface Electromyography

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86092951" target="_blank" >RIV/61989100:27240/12:86092951 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.4304/jcp.7.5.1099-1103" target="_blank" >http://dx.doi.org/10.4304/jcp.7.5.1099-1103</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4304/jcp.7.5.1099-1103" target="_blank" >10.4304/jcp.7.5.1099-1103</a>

Result language
angličtina
Original language name
Flexible Neural Trees for Online Hand Gesture Recognition using Surface Electromyography
Original language description
Normal hand gesture recognition methods using surface Electromyography (sEMG) signals require designers to use digital signal processing hardware or ensemble methods as tools to solve real time hand gesture classification. Some methods could also resultin complicated computational models, complex circuit connection and lower online recognition rate. It is therefore imperative to have good methods to explore a more suitable online design choice, which can avoid the problems mentioned above. An online hand gesture recognition model by using Flexible Neural Trees (FNT) and based on sEMG signals is proposed in this paper. The sEMG is a non-invasive, easy to record signal of superficial muscles from the skin surface, which has been applied in many fields of treatment and rehabilitation. The FNT model is generated and evolved based on the pre-defined simple instruction sets, which can solve highly structure dependent problem of the Artificial Neural Network (ANN). FNT method avoids complica
Czech name
—
Czech description
—

Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
—

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)