Hands-free Head-movement Gesture Recognition using Artificial Neural Networks and the Magnified Gradient Function

Author

King, L.M. ; Nguyen, H.T. ; Taylor, P.B.

Author_Institution

Fac. of Eng., Univ. of Technol., Sydney, NSW

fYear

2006

Firstpage

2063

Lastpage

2066

Abstract

This paper presents a hands-free head-movement gesture classification system using a neural network employing the magnified gradient function (MGF) algorithm. The MGF increases the rate of convergence by magnifying the first order derivative of the activation function, whilst guaranteeing convergence. The MGF is tested on able-bodied and disabled users to measure its accuracy and performance. It is shown that for able-bodied users, a classification improvement from 98.25% to 99.85% is made, and 92.08% to 97.50% for disabled users

Keywords

biomechanics; gesture recognition; handicapped aids; medical control systems; medical signal processing; neural nets; signal classification; artificial neural networks; disabled users; gesture classification system; hands-free head-movement gesture recognition; magnified gradient function; Artificial neural networks; Australia; Charge coupled devices; Convergence; Error correction; Feedforward neural networks; Neural networks; Paper technology; Testing; Wheelchairs; head-movement; neural network; power wheelchair control;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1616864

Filename

1616864